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CMU-Modeling Cultural Factors in Collaboration and Negotiation

MODELING CULTURAL FACTORS IN COLLABORATION AND

NEGOTIATION

Carnegie Mellon University, The Robotics Institute Pittsburgh, PA 15213

October 2007

PROPOSAL ONR BAA 07-036 In Response to MURI Topic #14: Human, Social, Cultural and Behavioral Modeling: Dynamic Models of the Effect

of Culture on Collaboration and Negotiation Technical Contact

Katia P. Sycara The Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 Tel: (412) 268-8825

Fax: (412) 268-5569 Email :[email protected]

Administrative Contact

Joseph Sullivan Office of Sponsored Research Carnegie Mellon University Pittsburgh, PA 15213 Tel: (412) 268-1161 Fax: (412) 268-6279 Email:[email protected]

Research Team Carnegie Mellon University: Dr. Katia Sycara, Dr. Baruch Fischhoff, Dr. Geoffrey Gordon Georgetown University: Dr. Katherine Tinsley, Dr. Robin Dillon University of Michigan and CUNY: Dr. Scott Atran University of Michigan: Dr. Robert Axelrod University of Pittsburgh: Dr. Michael Lewis, LTC Charles Grindle (recently back from Iraq) University of Southern California: Dr. David Traum Other DoD Sponsors Darpa: Dr. Tom Wagner 571-218-4309 AFOSR: Dr. David Luginbuhl 703-696-6207 ARO (ITA program); IBM subcontract: Dr. Dinesh Verma 914-784-7466

Complex Belief Models, Collaboration and Negotiation

Table of Contents Statement of Work p. 3 Technical Approach p. 4 Project Schedule, Milestones and Deliverables p. 23 Assertion of Data Rights p. 24 Management Approach p. 25 List of References p. 28 Letters of Support Curriculum Vitae


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1. Statement of Work The overall goal of this multidisciplinary research is to develop validated theories and

techniques for descriptive and predictive models of dynamic collaboration and negotiation that consider cultural and social factors. The domains will be the Middle East, Turkey, Iran, Iraq, Morocco. The research activities will be broken down into the following tasks:

Task 1: Design, use and testing of cultural investigation instruments for the construction of mental models/schemas that include cultural factors in the cultures of interest. This includes 1) extracting focus information from SMEs and analysts, 2) design of questionnaires for surveys, 3) design of questions for interviews with individuals, 4) use of techniques such as triangulation for in depth questioning, 5) use of anthropological techniques of participant observations that not only provide insights for analysis but also insights into constructing new survey questions that probe deeper on selected topics, 6) design of experiments that involve interactive cooperation and negotiation, 7) development of the process simulation software including multi-player cross-cultural games in-country or over the Internet, so that process trace data would be logged and collected

Task 2: Data collection in the cultures of interest. This includes 1) conduct pilot studies for method verification, 2) conduct the semi-structured interviews and surveys with the different subject populations in the countries of interest that most notably include militants and controls (that is, persons belonging to the same social networks and demographic groups as militants but who renounce violence); we have identified several such networks and are currently working with the respective government authorities (Turkey, Morocco) to provide cooperation and personnel, 3) conduct experiments that involve within-culture and cross-cultural collaboration and negotiation using the developed process simulations, 4) enrich these sources with additional information from the literature, observations of videotapes, information from SMEs

Task 3: Culturally Sensitive Analysis and Theoretical Model Formulation (culturally sensitive and process adaptive schemas). This includes 1) application of Cultural Consensus Modeling techniques to evaluate statistical reliability of response across subjects from different sample populations, and for evaluating the relationship between mental models and actual behavior, 2) use of additional statistical techniques such as Multivariate Analysis of Variance and Structural Equations, 3) development of new models and metrics that enable further analysis, 4) use of these models and metrics to determine the cultural factors that influence collaboration and negotiation in the cultures of interest, 5) use of the analysis techniques to determine how the culturally sensitive mental constructs adapt during the interaction, 6) creation of a grounded theory of the influence of cultural factors in collaboration and negotiation intra-culturally and inter-culturally, including models of interventions, and modeling how the influence of these factors may change with changes of the situational context, 7) test and validation of the theory on additional scenarios of interest, 8) SME feedback on the analysis and models

Task 4: Computational Model Formulation: This includes 1) extend the theory of games to take into consideration socio-cultural factors, such as Sacred Values and players embedded in complex social networks, 2) develop new game theoretic algorithms that enrich current models with communication, argumentation and learning processes 3) develop evaluation metrics 4) develop an integrated computational model of dynamic collaboration and negotiation with appropriate parametrizations 5) develop techniques for systematically deriving model parameters from the analysis of the data collection results 6) test and validate the model in scenarios of interests against the predictions of the theoretical cultural model and perform mutual revisions


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and alignments. As part of this activity SMEs and cultural scientists will provide feedback and guidance for model construction and possible adaptations

Task 5 Training Simulation and Virtual Humans. This includes 1) insertion of developed culturally sensitive computational algorithms into the virtual human models for training, 2) development of training scenarios 3) evaluation of training effects in the scenarios. SMEs and cultural scientists will provide guidance and feedback

Task 6 Education and Outreach. This involves 1) inclusion of research results in courses taught by members of the research team in their home institutions, 2) possible development of new course modules, 3) organization of workshops in conferences, reporting of research results in scientific journals and conferences, presentations to government stakeholders, tool demonstrations

Task 7 Management and Team Coordination: This involves management of the research team and effort coordination, team meetings, constricting and maintaining the project web-page, reporting results, planning tasks and research direction, results and tools validation and integration

Task 8 Transition Opportunities: This task involves 1) identification of end users, 2) providing information, models and tools to end users 2. Technical Approach 2.1. Introduction and Motivation In recent years, the US military has been asked to participate in multi-national teams

(coalitions) to cooperatively plan complex multi-national missions e.g. in the context of the Global War on Terror or cooperate with multi-national non-government organizations (NGO’s) in relief efforts and a variety of operations other than war (OTW). At the same time, the US military is asked to increasingly engage in negotiations with individuals, local and government organizations in Arab countries (e.g. Iraq, Afghanistan) in the context of Stability, Security, Transition, and Reconstruction Operations (SSTR).At a yet higher level, the US military also plays a role in government conflict resolution efforts, such as those concerning Iran. During SSTR, U.S military officers collaborate with coalition partners for mission planning; at the same time they conduct thousands of negotiations in Iraq and Afghanistan with civilian Iraqi or Afghan leaders, usually local mayors, sheiks, tribal leaders, town council members. These negotiations vary as to setting (some are conducted in formal meetings, some in the street, some during military operations, such as searchers, raids, checkpoints etc) [124]. Additionally, they vary as to the object of the negotiation: over security concerns, over detainees or hostages, seeking information about insurgents or terrorists, cooperation in supporting elections, scope of reconstructive efforts, terms of reconstruction or supply contracts. One important characteristic that differentiates these interactions is the fact that violence often exists in the background of the negotiations, thus lending additional significance to the overall negotiation context.

In dynamic collaboration and in negotiation, cultural differences of the parties are an important factor that affects the process and outcomes of these interactions [1,11,21,84,115,117]. In particular, in coalition operations, challenges range from not realizing the potential benefits (variety of perspectives and skills could enhance creativity and number of alternative solutions ) of the diversity of the team to misunderstandings that may slow down decision making, and lead to suboptimal outcomes [70,84,85]. In negotiation, the effect of culture could range from not achieving good outcomes to having bad outcomes (e.g. turning previously neutral Iraqi leaders into enemies) [124]. Therefore, studying and understanding the role of cultural factors in dynamic cooperation and negotiation is crucial.


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The overall goal of this multidisciplinary research is to develop validated theories and techniques for descriptive and predictive models of dynamic collaboration and negotiation that consider cultural and social factors. In particular, we aim to: • Generate theory on the cultural factors that influence dynamic cooperation and

negotiation and the underlying mechanisms for Iran, Turkey, Iraq, Middle East, Morocco and other Arab cultures. • Develop and validate culturally sensitive models that embody the theory and could

predict participants’ behavior during collaboration and negotiation and analyze the similarities and differences of cultural factors across groups and sub-groups (e.g. Jihadis vs controls) in the cultures of interest The validated model would also identify possible interventions that would dynamically influence the behavior of the participants in order to promote increased collaboration and successful negotiation among members of teams (including coalitions), neutral, or unfriendly entities (individuals or groups). • Develop and validate computational algorithms that embody these models • Implement software that incorporate the validated algorithms and use them for prediction

of performance, dynamic training and mission planning. A necessary element of these goals is the development of measures of dynamic collaboration

and negotiation, as well as performance metrics for assessing outcomes and process. To perform the research and fulfill the research goals we have assembled a multi-disciplinary

team of cultural anthropologists, psychologists, organizational behaviorists, decision scientists, communications scientists and computer scientists.

We envision the following overall methodological research approach. Our team will use proven methods to collect relevant cultural, demographic, socio-political and historical data including working with SMEs, data gathered through experimental surveys in Iran, Turkey, Palestine, Morocco and other Islamic countries, we will gather process data from simulations and web-based multi-cultural player games. Analysis will be done using state of the art statistical techniques (e.g. Multivariate analysis of Variance (MANOVA), Cultural consensus Modeling (CCM)). The results of the analysis will be (a) used to create new theory of the effect of cultural factors on collaboration and negotiation in the cultures of interest, (b) incorporated into a novel computational model that combines the best features of graphical models, first-order logic, and game-theoretic models. The model will be able to capture trust, power, sacred values, communication and argumentation, all of which have been found to play an important role in human collaboration and successful negotiations. The role of intermediaries will also be studied, as there is evidence in the conflict resolution literature that trusted intermediaries play a crucial role in conflict resolution in many cultures. The model will be integrated and implemented into software artifacts for training and operational planning. The software will be validated and evaluated for performance prediction and training using realistic operational scenarios. Note that these steps are not strictly sequential but will allow for insertion of feedback and lessons learned in a continuous cycle that involves tight cooperation of the research team.

The research will deliver multiple results. First, we will deliver new theoretical advances in inter-cultural dynamic collaboration and negotiation that would shed light on cultural factors affecting these interactions in the cultures (and sub-groups) under study. Second, we will deliver an integrated framework (a) within which different types of inter-cultural cooperative and competitive interactions can be studied, (b) which could provide a way to identify and execute successful mitigations and interventions, and (c) provide a unified perspective for viewing different insights and research findings to-date. Third, we will deliver a set of computational


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models and tools that would be used for analysis of different scenarios of interest, conducting what-if studies, prediction of behavior and training.

2.2. Technical Rationale The research will fill a number of science gaps and overcome current limitations Although the literature on collaboration and negotiation is vast, the subject populations to-date

have been either (mostly US) university students or business managers. Therefore, the resulting insights may not be directly applicable to dynamic collaboration and negotiation issues that are relevant to the military. We will collect relevant data (e.g. cultural beliefs and norms on radicalization and violent conflict resolution) from the subject populations of interest.

Although inter-cultural negotiation studies have been done in recent years, especially for East Asian cultures, there is a dearth of literature on cultural factors of collaboration and negotiation in Arab cultures. Our research will engage in collecting data from these cultures, and use the analysis results as inputs to theoretical models of collaboration and negotiation involving intra and inter-cultural interactions in these cultures.

Currently, the literatures and research communities on dynamic collaboration and negotiation are disparate. In the computational literature, teamwork models include joint team goals and team plans of how to achieve team goals, but do not model conflict resolution. On the other hand, the computational negotiation literature considers Nash equilibrium as the only criterion for outcomes. Human collaboration and teamwork models concentrate on teamwork goals, information sharing for forming common mental models, monitoring of team activity and helping team mates, but do not include conflict resolution and negotiation as part of team activity. The big majority of human models of negotiation consider collaboration (integrative bargaining) to the extent that the players maximize joint gains but do not consider “other-focused” behaviors, e.g. helping the other party get what they want, or considering overall common goals. Our work will develop an integrated theoretical and computational framework that accounts for a continuum of collaborative and conflict resolution behaviors in intra and inter-cultural settings of interest.

Current computational game theoretic models rely on assumptions of player rationality to compute outcomes in terms of Nash equilibria. However, multiple studies [7,11] have shown that assumptions of rationality and outcomes expressing Nash equilibria are not adhered to in human negotiations. Furthermore, even under perfect rationality, equilibrium considerations are not enough to predict agent actions: most realistic games have multiple equilibria and each agent has no way of knowing which equilibrium the other agents are following. So, we propose to enrich game theoretic models with communication, argumentation and learning algorithms to allow agents to find various types of stable equilibria considering various degrees of agent irrationality.

Current computational models of collaboration and negotiation do not consider modeling of cultural factors, or make connections between real events and model parameters. Models developed by social scientists try to make such connections, however these models are often simplistic. Our research will use cultural assessment techniques (e.g. Cultural Consensus Modeling, [13,14]) to develop sets of actions, and inputs to decision algorithms in the computational models. The cultural theories will be the source of testable hypotheses that will guide parameter selection and also validation studies.

Current work in negotiation has been mostly concerned with the outcome of negotiation. However, it is clear that, especially in cross-cultural interactions, the process is also of great importance. Moreover, most prior work (a notable exception is work by our team [2,115,120]) has not considered adaptation in the cultural cognitive models of the parties during the


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interaction. In the proposed work, we will provide methods for assessing such cognitive changes and ascertain which cultural factors are most important in influencing adaptations.

Current work in collaboration and negotiation is limited by its inability to seamlessly explain phenomena across individual, local, and national dimensions, e.g. negotiations with different granularity - among nations, organizations, individuals. Basic research is sorely needed to (a) characterize common features and cultural invariants along these dimensions, and (b) determine changes in participant behaviors that are observable and actionable along these dimensions. We believe our approach will enable progress in this respect.

Current work on human negotiation has identified important dimensions and assumptions that govern the parties interactions, e.g. parties’ goals, conflict resolution strategy, persuasion tactics etc. Since most research work is based on Western scholarship, these dimensions may not comprise a complete list (or may not be as important) in the cultures of interest. Emerging work (notably by our team [7,8,11,42]) in non-Western cultures has started to identify additional assumptions, such as the importance of Sacred Values as a fundamental context within which conflict resolution involving Islamic cultures can be conceptualized. We will take a deeper look into the effect of Sacred Values in this effort and hope to further enrich theory with new discoveries..

Current work on intra and cross-cultural human conflict resolution has not provided a theoretically grounded model of where in the process and what type of interventions could be effected so as to influence the parties’ behaviors. We propose to address this issue through the theoretical postulation of conflict resolution cognitive schemata of the participants through which they view the interaction [1,2,115]. This framework allows for the identification of points of insertion of interventions and their evaluation.

2.3. Theoretical foundations The proposed research draws from three strands of scientific research: research on teamwork

and collaboration for high performance decision making, research on negotiation and conflict resolution and research on culture. Research on teamwork effectiveness has produced key behaviors of team competence (e.g. back-up behaviors, performance monitoring, team orientation) [24,95] that have been found to be critical to team performance. Research on negotiation has identified fundamental components such as goals, negotiation strategies, persuasion, information gathering etc.[21,117,120]. Most of the research findings and models are the result of research in Western cultures. Mounting evidence suggests that culture affects collaboration and negotiation [1,2,11,22,119] in important ways.

Culture is a distinct set of characteristics of a social group that provides shared values, institutions and norms that help members interpret and react to recurring social situations, such as dynamic collaboration and negotiation [11,14,15,21,74,117]. Pioneers in cultural psychology sought to characterize cultural differences in terms of a small number of relevant dimensions (e.g., individualism versus collectivism, uncertainty avoidance, power distance, see [52,55,125]). A complementary view [12,13,15,16] aims not at establishing some true definition of culture in terms of a set of dimensions but rather at understanding the factors, underlying causes and dynamics that lead to agreement patterns (e.g., social norms) within a society. This view, operationalized in cultural consensus modeling, aims to explain how decentralized local interactions of heterogeneous individuals lead to the emergence of macroscopic societal regularities such as social norms while admitting that there are feedback mechanisms between the macro- and micro-structures.


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We propose to use an integrated methodological approach that includes appropriate methods (e.g. cultural consensus model) to elicit mental models of individuals and concordance of beliefs concerning cultural factors that would be influential in dynamic cooperation and negotiation in Turkey, Iran, Israel/Palestine and Morocco. This approach can also be used to examine within and across-group differences. Our data collection will be also guided by macro-structure cultural dimensions, e.g. individualism vs collectivism, uncertainty aversion etc. Such an integrated approach will allow (a) the discovery of relevant cultural patterns across and within cultures, (b) modeling of the feedback mechanism between individual cognitions and social beliefs and (c) furnishing culturally sensitive plausible patterns and principles for individual behaviors and decisions in collaboration and negotiation

We will use the integrated methodology to construct a theoretical framework that views dynamic collaboration and negotiation in a unified way. We posit that dynamic collaboration (e.g. coalition planning) and negotiation have crucial common elements. These are: (a) conflict resolution (this has been called divergence in team processes where alternative viewpoints are presented and debated/negotiated by the team), (b) process of concession making (in dynamic cooperation this has been called process of convergence), (c) a negotiated agreement (in dynamic cooperation this has been called decision point) with (d) particular expected outcomes, where the parties (e) commit to a set of actions, and finally (f) execution of the agreed upon activities. The particular attitudes and observed behaviors of the parties during these phases are context- and situation specific, however the main point here is that these two social interactions need not be viewed as separate and distinct from each other. This gives rise to our overall theoretical quest for the creation of an integrated theoretical model of dynamic collaboration and negotiation and the influences that cultural factors have on its various aspects.

2.4. Research Approach 2.4.1. Overall Framework of Dynamic Collaboration and Negotiation

Dynamic collaboration and negotiation can be viewed as processes of interdependent interaction by two or more parties (for simplicity we show 2 in Figure 1). The interaction has an undetermined number of rounds; it has a relatively stable number of players (generally, there is not a lot of entry or exit of players; but we do consider third party intermediation and also players’ accountability to different constituencies). We are interested not only how cultural factors influence the outcomes of the dynamic cooperation or negotiation, but also how they influence the process. We are interested in formulating a theory of how culture is reflected in the players’ cognitions during the process of dynamic cooperation and negotiation. A player may not necessarily be an individual representing his own concerns but could be representing concerns of a societal group (e.g. an institution, an enterprise, a tribal segment). 2.4.2. Cognitive Schemas in Dynamic Collaboration and Negotiation

We postulate that the behavior of an individual and the interpretation of the behavior of the “other” depend on the schema/lens/mental model through which the individual sees the world. A schema is a mental model that stores an individual’s reservoir of declarative knowledge, moral values, beliefs, meaning information about facts, persons, and things [36,92] and is organized by culture into coherent patterns and meaning. This schema knowledge is learned by members of a social group through both instruction and experience and thus is culturally constituted [68,116,118]. A schema 1) filters information coming from the environment, 2) helps individuals make sense of, or give meaning to, this stimuli and 3) guides outgoing reactions [118]. Cognitive schemata (mental models) have been postulated both in negotiation literature [117,120] and in collaboration and teamwork literature [70,95,98,102,105]. Hence, in our work,


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they will constitute the common cognitive construct and building block of our unified theoretical framework.

Our hypothesis is that schema activation is context specific and situation specific.. Given a particular context of interaction (e.g. mission planning, negotiation over hostages), a suitable schema will be retrieved and activated. Situational cues, such as the nationality of the other party, are an important catalyst for schema activation [2,77,115]. Additional cues include the history of interaction between the parties, and the type of issues (e.g. sacred values, material values) [2,11,36,80]. As such, participants are likely to distinguish between intra- and inter-cultural interactions (collaboration and negotiation) rather than holding a generalized schema for all such interactions.

Figure 1 illustrates how schemas filter and interpret incoming stimuli and guide outgoing reactions for a simple two-party (Parties A and B) interaction e.g. for resolving a conflict. Party A’s schema enters in two places. First, it is the lens through which party B’s behavior will be interpreted and second it is the filter through which A’s actual intentions will give rise to concrete behaviors visible to the other party. In this way, schemas become relevant whenever an individual is taking information from the outside world or offering behavior to the outside world. A’s culture and A’s history of interaction with B (or members of B’s culture) will influence party A’s schema. Important components of a schema are goals (what is appropriate to try to achieve), norms (what is appropriate behavior to go about getting what you want), and beliefs and attributions about the character of the other person –B [2]. A’s schema includes “who B is”, which influences A’s interpretation of B’s Behavior “what B is doing”. This drives A’s intentions or strategies for the next move. Should A be cooperative or not? A’s intentions will then drive A’s behavior, as filtered again through A’s schema, which, recall, includes norms for appropriate behavior. Since this is a symmetric situation, B’s schema filter’s A’s behavior and influences how B interprets that behavior, which influences how B intends to respond (i.e. B’s strategy choice—cooperate versus not) and then B’s behavior, as filtered through B’s schema of behavioral norms. Therefore, difficulties in inter-cultural collaboration and negotiation could be due to incompatibility in the content of schemas (ie the culturally determined assumptions) that the parties bring to the table [22,115]. 2.4.3. Schema Adaptation

Note that Figure 1 takes into account that parties interacting in a cross-cultural context react to that context differently (bring different conflict resolution schemas to the table) than if they were interacting in an intra-cultural context. This distinction is quite important because most cross-cultural work tends to recognize that members from different cultures espouse different conflict resolution schemas, but fails to recognize that players shift (or adapt) their schemas to the cross-cultural context.

By contrast, our research aims to account for, not only what are the mental schemas that the parties bring to the table, but also how these schemas influence the interactions and are adapted during the interactions. Consequently, the first overall part of our research will be directed at understanding the content of all the cognitive constructs in Figure 1 (e.g. goals, assumptions, norms, beliefs) that the cultures under study bring to the table and how these contents are influenced and adapted during the interactions. This means: 1) developing an understanding of the Arab culture conflict resolution schemas, 2) Developing an understanding of Arab person schemas for Americans, and vice versa. 3) Modeling how these schemata influence both Arab and U.S. interpretation of a concrete set of conflict resolution behaviors 4) Modeling how they respond to this understanding and why—meaning what behavioral response is elicited from a particular behavior by the other party and why did participants think that response was justified


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A’s culture

A’s history with B

Context

B’s culture

B’s history with A

ContextB’s behavior

A’s interpretation of B’s intent

A’s real intent

A’s behavior

B’s interpretation of A’s intent

B’s real intent

B’s schema

A’s schema

B’s schema

A’s schema

Interventions

Behavioral modification interventions

Reframing interventions

Figure 1: Role of parties’ cognitive schemas in dynamic collaboration and negotiation. In previous work with East Asian and US managers (both of which had inter-cultural

negotiation experience), negotiators have been found to adapt their culture-specific negotiation behaviors in inter-cultural negotiations [1,2]. Moreover, our team [115,120] has documented this schematic shift when parties enter an inter-cultural context, and find that inter-cultural interaction difficulty is not necessarily because neither side adjusts their schema sufficiently but rather because BOTH sides over-compensate when adjusting to their stereotypic notion of the other party. This has obvious and significant implications for training. For example, training should emphasize the fluidity of the other party’s schema and their likely adaptation to the inter-cultural context rather than over-emphasize U.S. cultural stereotypes of other cultures. 2.4.4. Process Interventions

The dynamic model of interaction, depicted in Figure 1, also indicates where interventions can occur. There are two possible points of intervention. The first would be a ‘reframing” intervention, targeted to reframing one party’s interpretation of the other party’s behavior. What may have been interpreted, for example, as A being disrespectful, could potentially be “reframed” for B as having another interpretation (asking about your daughter was not disrespectful but rather was a behavior designed to show respect for your kinship). The other point of intervention would be a behavioral modification intervention targeted at changing one party’s response to a particular interpretation of behavior. That is, B may still find A’s behavior disrespectful but rather than retaliate with his own “threat”, will instead couple a “threat” with an interest-based proposal.

Thus, the second part of this research is aimed at discovering and testing various intervention methods and seeing how they change the dynamics of the model. Additional important issues are to (1) determine how historic interactions affect formation of cultural stereotypes and (2)


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determine whether it is possible to influence the schemas (either before or during the interaction) towards a more trusting direction that could enhance collaboration. In order to examine these issues we will (a) determine intercultural conflict resolution schema that a player brings to the table. (b) check for updating of intercultural schema (c) link updating (or not) to the conflict resolution process, ie what is going on in the interaction phase-- is there resistance to updating based on self-fulfilling prophesies? (d) link updating to characteristics of parties – are more experienced better able to update? The methodology will primarily rely on questionnaires, process simulations to gather process data followed by post process questionnaires on intercultural schema of the participants. (see also section 2.4.6) 2.4.5 Fundamental Elements of Conflict Resolution Schemas

The contents of the conflict resolution schema reflect important decisions that the parties must make during the interaction: These are: (a) who is the other party (or who does he represent?), (b) goals of the parties, i.e. what kind of outcome they want to accomplish, (c) what overall strategies they will adopt to reach their goals, (d) persuasion, i.e.. how to convince the other party to accept a proposal, (e) information sharing, ie how to get information from the other party. The way these issues are addressed has been found to vary in different cultures (e.g. individualistic vs collective cultures) [125,21,22,117]. The contents of the schema, however, may not comprise a complete list of what is fundamental to conceptualization of negotiation and collaboration. Emerging research in non-Western cultures is starting to identify additional assumptions, such as the importance of Sacred Values [7,11] for conceptualizing conflicts and their resolution. Therefore, one of the foci of the research will be not only to identify the cultural factors of the variants of Arab cultures of interest on the above identified issues, but also to perform research on newly identified elements, such as Sacred Values. Moreover, we will be searching for any additional fundamental elements of collaboration and negotiation that may not have been identified by current research, such as how much risk a decision maker is willing to accept to try to secure his preferred outcome. Identifying who the other party is or who does it represent?

Identification of who the other party is/represents (the type of the other, in game theoretic language) is an important factor in determining the party’s goals, priorities, and interaction strategies. In Western societies, it is assumed that the person is isolated from the role it has in interactions. This has two consequences (a) the person will get into the role that the interaction requires (e.g. he will represent his own interests in a person-to-person interaction; he will represent the interests of his organization in a business interaction etc), and (b) interaction among strangers that may not trust one another is possible (and is typically the case). In non-Western cultures, where the person is embedded in different social networks, (a) it is often the case that negotiation interactions happen only within the social network, and (b) when interactions happen the role of the other party may not be clear but must be elucidated during the interaction. This is consistent with the experience with Arab negotiators, who will spend long time in the initial phases not talking about the issues but engaging in small talk. Although US negotiators mis-perceive this behavior as idle talk, the Arab counterpart uses this talk to get to know the character and preferences of his counterpart and building “trust” in him to the extend that it gauges the chances of the other party keeping its commitments. Issues of accountability could also be relevant here since Arab cultures are collectivistic.. Accountability to constituents (e.g. family, tribal group) is also an important factor in cases of third party intermediation. We propose to study issues of accountability and third party mediation, and investigate to what extent social networks influence the role of parties in the Arab cultures under study.

Parties’ Goals and Outcomes:


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Parties goals are what type of results the parties are aspiring to. It has been found that in individualistic cultures goals tend to concern material values (usually economic benefits) and be oriented towards self interested gains (getting a good deal for oneself) [86,87,114]. “Other-focused” goals, such as maintaining relationships (including building mutual liking and trust, and mutual commitment to the relationship, getting a good deal for both parties) and altruistic behaviors (getting a good deal for the other party) are important in collectivistic cultures [46,125] and will also be important in team work and dynamic collaboration. The dominance of relational concerns has the following consequences:

1) Relational concerns may be inconsistent with achieving economic capital, at least in the short run. To build trust and mutual commitment to the relationship parties may have to forgo current gains and even incur some costs. It is not that there is non-expectation of payback in those cultures. Reciprocity appears to be a universal norm [2,25]. It is just that payback is not viewed in the form of the immediate accumulation of economic capital but could come into play in the future [21]. We will test this hypothesis for the Arab cultures of interest.

2) Trust plays a crucial role, especially in competitive interactions where the other players may not be trusted to keep their commitments or tell the truth about the deals they propose or arguments they make. In iterated games, trust has proven useful in allowing agents to reach higher-payoff Nash equilibria. Research has found that willingness to trust can vary among cultures. Trust also caries risks of vulnerability, which has been called betrayal aversion [19,20]. Lack of trust can lead to negotiation breakdown and attitude shifts from win-win to win-lose interactions. Ways to increase trust (from research in Western cultures) include: establishing credibility and good reputation; “speaking the other’s language”, not only understanding the terms but understanding the nuances; justifying one’s concessions and demands while emphasizing common gains; acts of reciprocity; unilateral concessions, or symbolic concessions for issues that are sacred values. We will investigate whether these means of increasing trust are also prevalent in the cultures of interest.

3) Dominance of relational concerns increases the importance of trusted intermediaries to perform the negotiation. In East Asian cultures, third parties are frequently involved because they take the whole context (e.g. concerns about the social network where the actors are embedded) into account. In prior work, we have created mathematical models of mediated negotiation [60,109,110] and found that a mediator can help the parties reach Pareto optimal outcomes in multi-issue negotiations. We plan to collect cultural data on mediator role in negotiations and identify potential effects in Arab cultures.

4) Part of the concern over relationship (as opposed to economic capital) is that the parties take care during the process to understand the interests and preferences of the other party. Prior cross-cultural research [40] has found that satisfaction with inter-cultural negotiation was significantly correlated with the parties’ respective satisfaction with how well their counterparts understood their interests during the negotiation, independently of the material outcomes. Understanding how these process constructs affect within and across-culture dynamic collaboration and negotiation in Arab cultures is a research topic we will pursue. We will determine parties’ goals in different cross cultural collaborative and competitive situations for the cultures of interest. Risk considerations: Prospect theory [56] says that that people’s evaluation of a particular prospect is largely a function of their reference points, defining gain, loss and risk as opposed to the expected utility of the gamble. In this work we propose to utilize decision-related risk analysis to identify cultural factors that affect risk evaluations [31,32,33,34,35]. First, we will create a multi-attribute


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characterization of consequences that can be used to assess risk (and benefits)[82]. Second, we will identify the kinds of statistical data that might be sought in order to derive quantitative estimates of the outcomes of critical situations, as characterized by anthropological and political science perspectives [81]. Third, we will build a qualitative formal model of the processes creating and controlling risks, of the sort that can be used to create structured scenarios and design response strategies. The result of this endeavor will be to render the social science results in a form that will be more accessible to quantitative modelers. Choosing Overall Conflict Resolution Strategy: the Unique position of Sacred Values

Prior work by our team on the Israeli/Palestinian conflict [7,8,11,42] has found that Sacred Values play a major role as frames/paradigms which define the kinds of overall strategies and the kinds of agreements that can be negotiated. Interesting new theory and counter-intuitive findings have already emerged.

Models of individual and group based choices have tended to assume that theories of bounded rationality can explain choices of collaborative or competitive strategies. However, our research team has found that when identity relevant sacred values (SVs) are threatened or violated by another group, decisions to adopt cooperative/diplomatic versus competitive/violent strategies tend to be driven by moral intuitions [47,48] rather than deliberative consequentialist judgments [7,8,11,41,42]. Sacred values differ from instrumental values by being immune to material tradeoffs and incorporating moral (including religious) beliefs that may drive action independently of, or out of all proportion to, its prospects of success [42,43,96,113,126]. Many cross-cultural collaborations implicitly or explicitly involve threats or compromises to SVs [16,51,88]. Therefore, to understand, model and predict cooperative and competitive choices in cross-cultural interactions we will apply our emerging understanding of moral decision-making and SVs to a broader investigation of the cognitive processes involved in cross-cultural contexts.

Our broad goal is to investigate the way Sacred Values influence cooperative and competitive choices in cross-cultural collaborations and interactions in Turkey, Iran and other Arab cultures. We have operationalized this goal into three research objectives:

1. Identify a cross-cultural taxonomy of Sacred Values. Past research into SVs has typically categorized a value as sacred when a community declares that this value is immune to cost/benefit considerations [42,112,113]. However, there are indications in our own research that not all SVs are treated in the same way. Threats to some SVs cause stronger levels of competitive responses than others, while different strategies to induce cooperation over competing SVs work more effectively in some cases than others. To investigate this issue we will begin by a cross-cultural investigation of the taxonomy of SVs.

2. Identify individual and group differences. Although we expect a great deal of cultural consensus with respect to categorizing a value as “sacred” we do expect to find sub-group differences within cultural groups as well as individual variation. With respect to sub-group differences we will examine whether specific groups differ in which values, and which types of values, are considered sacred. We will be investigating two ways in which individuals within social and cultural groups may be expected to vary: cultural competence and radicalization. Cultural competence refers to the degree to which an individual correctly identifies values considered sacred in a given context. Radicalization refers to the individual differences in the level of violation or threat to SVs which is perceived to mandate competitive/violent responses. Our preliminary research has found for example, that radical members of Hamas often rank duty to violent jihad above belief in God.

3. Achieving cooperative outcomes in collaborations/negotiations implicating threats to Sacred


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Values. We will examine cultural, group and individual differences in the adoption of competitive versus cooperative strategies in collaborations/negotiations implicating different types of SVs (typically human, typical of in-group, distinctive to in-group, essential for in-group; harm/care, fairness/reciprocity, in-group loyalty, authority/respect, purity/sanctity). To examine means of achieving cooperative collaborations involving SVs we will investigate the following:

A. Modes of symbolic compromise In [42], we demonstrated that material compensation for a loss of a SV backfires, resulting in heightened competive/violent responses, while moral trade-offs (where the other group sacrifices their own SVs) seem to result in more cooperative outcomes. However, we do not yet know the extent to which these findings will be replicated cross culturally, whether they apply to all types of SVs and we do not yet have a clear understanding of the types of moral trade-offs that will work. Using surveys we will explore different types of symbolic compensation of the other party to threatened SVs.

B. Order effects and symbolic compensation In [42] symbolic “other” compromises decreased opposition to compromise over SVs. In these studies, compromise was simultaneous. In the real-world compromises are often sequential. In [43], we showed that inter-ethnic cooperation over material resources is highly unstable when one group cooperates first. We will examine whether this holds in cooperation over SVs.

C. Order effects and material compensation In interviews we have conducted with leaders of Hamas, participants were presented with consecutive peace deals, one involved trading off a SV for peace (taboo), the next involved the same taboo trade-off with material compensation (taboo+) and the last involving the taboo trade-off with parallel symbolic Israeli trade-offs instead of material compensation (symbolic). Most participants rejected the taboo deal, and rejected the taboo+ deal even more strongly. When presented with the symbolic deal, participants were prepared to consider the trade-off but argued that material compensation was also necessary [11]. To focus on the irrationality of these answers may miss the point. It appears that symbolic compensation may open the way to stronger support for compromise that includes necessary material compensation. To test this idea we will present participants these three types of deals consecutively, varrying the order and measuring support for the compromise, as well as support for violent opposition to each compromise.

D. Mediations by third parties In addition to examining the impact of different types of material and symbolic compensation on collaborations when offered by an adversary, we will also investigate the impact of indirect negotiations over SVs where compensation is either made or mediated by third parties.

Strategies to persuade and influence the other party An argument is a communication that allows a player to justify his/her negotiation stance, and

influence another’s negotiation stance [106,108]. In Western cultures, the assumption of rationality prescribes that the parties offer rational arguments (including promises and threats) as to why the other party should agree with their view or make some particular concession. By contrast, in collectivist cultures [27,67] the dominant way to convey arguments is through emotional appeals (e.g. remind the “other” of their status and responsibilities in the social order). There is practically no research on argumentation in Arab cultures. This is a gap we will fill. We will determine the influence and means of persuasion in Arab cultures. We will determine what possible arguments may be effective in Arab cultures especially in the presence of SVs. Strategies to gather and share information

Knowledge of the other party’s preferences and priorities is essential for the interacting parties [114]. The western paradigm on information exchange is “low context” i.e the negotiator


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explicitly asks the other party questions about his preferences and priorities, (assuming he is telling the truth), and reciprocates with information about his own preferences and priorities, thereby building an understanding of the tradeoffs and formulating multi-issue proposals to capture the tradeoffs. Research [1,91] in non-Western cultures (Japan, Thailand) has found that instead of direct questioning, the parties make single and multi-issue proposals and draw inferences about the other parties’ priorities from the patterning of proposals and counter-proposals. We plan to study the information sharing practices in the variants of Arab cultures under study. In particular, we will examine whether the information sharing correlates with cooperative or competitive strategies, frequency of reciprocal offers and degree of joint gains.

2.4.6.. Subject Populations and Methods

2.4.6.1. Data Collection Methods Methods include laboratory and field experiments, population surveys along with field

interviews with individuals. We will use controlled and matched-sample studies that use reliable procedures for asking meaningful questions from proper samples of real world populations that may help us understand the different positions along the dimensions of collaborative and competitive/violent conflict resolution.. Many of the questions we have developed are also currently being used in mass surveys of Palestinians (Hamas vs. non-Hamas, refugees vs. non-refugees) by the Palestinian Center for Policy and Survey Research (under our own NSF research funds) and in Saudi Arabia, Morocco, and elsewhere by the University of Maryland (Department of Homeland Security Center of Excellence). Subject populations include:

The Middle East. This work will primarily involve surveys with built-in between-subjects design. We have previously designed experiments run through mass surveys and trained personnel in the Middle East to accomplish this. We will run the surveys with N = 1200-1500 Palestinians and N = 1200-1500 Israelis (for a sample see URL)

Turkey, North Africa, Iran. These studies will involve both semi-structured interviews and experiments (where possible) with militants and controls (that is, persons belonging to the same social networks and demographic groups as militants but who renounce violence). We have identified several such networks and we are currently working with government authorities to provide cooperation and personnel. A memorandum of understanding between our research team and the Turkish government has been agreed upon and the Moroccan government has informed us that an agreement should be ready by end of 2007 (see http://www.cs.cmu.edu/~softagents/proposal/). Surveys: The sampling process goes through three stages (1) randomly selecting population locations (clusters or counting areas) using probability proportionate to size; (2) randomly selecting households from the population locations using updated maps; (3) selecting a person who is 18 years or older from among persons in the house using Kiesh tables' method. The sample is self-weighting, but we also check that the age groups we obtain are similar to those in the society using data from official government statistics (if we can have access to them). To maximize the chances to enter all homes in the sample, two fieldworkers, a male and a female, conduct every interview. The non-response rate is typically 2%. To encourage respondents to talk freely, we assure them complete anonymity. (After extensive discussions with IRBs at the U. of Michigan and John Jay College, we were granted waivers of participant individual informed consent agreements in prior mass surveys with these and other populations). Interviews. Mutually reinforcing procedures bolster reliability of interview data. These are roughly grouped under: 1) Triangulation – In depth questioning with members of Jihadis and


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controls, and samples from communities supporting them, follow standard anthropological practice of participant observation, and are also structured in order to provide insights into constructing new survey questions that probe deeper on select topics. Many of the same questions will be used both in population surveys and in the interviews with individuals. 2) Generality- Questions are piloted to be transparent (if subjects ask interviewers to interpret a question, the question is discarded).

We will ask participants questions to understand their positions on the relation between political violence and cultural values. We will also ask questions to understand why some people become radicalized and others in the same cultural sub-group do not. We will ask participants to rate the appropriateness of certain interaction behaviors on a 7-point Likert scale anchored by “not at all” and “very much”. To examine possible parametrization and different parameter values for collaboration and negotiation schemata, we will conduct streams of surveys with various scenarios of collaborative joint planning and negotiation. In each scenario, participants will answer appropriate questions that will be designed to prime different cultural contexts. For example, when asked “In negotiation/collaboration, to what degree is it acceptable and appropriate to …”, we will prime participants to think about 1) negotiating/collaborating with someone from their own culture (to measure intra-cultural pre-negotiation schemas) and 2) negotiating/collaborating with someone from another culture (to measure intercultural pre-negotiation schemas). In addition we will measure parties’ stereotypic expectations of the other culture by asking them to rate the degree to which they thought parties from the other culture would deem each behavior to be appropriate in collaboration or negotiation. We will measure self-interested behaviors (e.g. getting a good deal for oneself) and other-interested behaviors (getting a joint good deal) and altruism (getting a good deal for the other party). In addition, we will measure persuasion, power (e.g. threats) and hierarchical persuasion (based on title, status, social network), information persuasion (based on facts or logic); information sharing ( revealing underlying preferences, reciprocating in sharing information) and other factors that have been found in the literature [1,2,119] to be culturally different. Multi-Player Simulations. We will collect process data during simulated collaborative or negotiated interactions. This method will utilize e-mail interactions, between pairs [40] or Web-based multi-player interactions. After the process data have been collected, we will also collect post-game data to determine intercultural schemas, satisfactions with the outcome and the process. We also plan to perform various manipulations of the task, e.g. examine the influence of stress and risk to oneself (to one’s in-group) on the interaction in order to find out whether stress and risk exacerbate negative stereotypes of others (versus baseline control without stress or risk). Interviews with SMEs. For Iraq, we will interview SMEs (LTC Grindle is part of our team; in the course of the research we hope to gain access to additional SMEs) and review Iraq-relevant materials (e.g. reports, video tapes). 2.4.6.2. Analysis Methods

We will utilize appropriate techniques such as factor analysis, Multivariate Analysis of Variance (MANOVA), Structural Equations to determine different factors that affect issues and differences in the schemas under consideration. In addition, we will utilize Cultural Consensus Modeling. Where applicable, responses from interviews are numerically coded in order to be assessed by CCM, which was designed by anthropologists and psychologists to assess the statistical reliability of responses across subjects from relatively small sample populations [90]. CCM is an effective tool for uncovering both shared and unshared knowledge. In some of our studies as few as 10 informants were needed to reliably establish a consensus. In cases of an


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existing consensus, the CCM justifies aggregation of individual responses into a “cultural model,” which estimates levels of agreement among the informants.

In previous work [13,14,15], our group has developed extensions to CCM techniques to increase their domain of applicability. In the proposed research, we will further extend the CCM to analyze data from our dynamic simulations, to gain the benefit of the richer cultural data present in these more complex interactions.

2.4.7. Computational Models of Dynamic Cooperation and Negotiation

When agents cooperate or negotiate they need to reason about their own and the other players’ beliefs about: plans and world state; desires and goals; uncertainty of the environment; negotiation strategies (rules, customs, external influences, plans for concessions); rationality of their opponents; and whether it is worth behaving irrationally (or seeming to do so). Various techniques have been proposed for reasoning about subsets of the above issues. These techniques however suffer from limitations, e.g., Bayesian games address uncertainty and agents’ goals but assume that the agents interact at a single time step; partially observable stochastic games add the ability to reason over time, but can be extremely computationally demanding; iterative negotiation models do not address interleaving proposed deals with actions in the world; and multi-agent influence diagrams (MAIDs) [58] and networks of influence diagrams (NIDs)[38] cannot handle statements with quantifiers, such as “many Iraqis distrust most American soldiers.” Additionally, MAIDs and NIDs suffer from reliance on the concept of Nash equilibrium, which is insufficient to predict agent behavior due to the equilibrium selection problem: there are typically infinitely many equilibria and infinitely many different corresponding behaviors, and the agents have no way to predict which one will occur in practice.

Building on our previous work [44,78,79,60,61,65,104,105,110,111], we propose to develop a model that combines the best features of graphical models, first-order logic, and game-theoretic models. The model starts from a knowledge base of first-order probabilistic statements about the world, players’ motivations and beliefs, and modeling assumptions. It then compiles this database into a propositional representation which generalizes extensive-form games and MAIDs. Inference within this propositional representation allows our agents to reason about uncertain properties of the world or of other agents. However, instead of seeking to discover other agents' strategies by finding an equilibrium, we propose to develop learning and communication algorithms that work on this representation.

In particular, we intend to use online, no-regret learning algorithms [44] to give agents the ability to discover rational behavior. While these algorithms guarantee to find various types of equilibria if all agents are rational, they also provide useful guarantees when some agents are behaving irrationally, unlike algorithms that are based directly on computing equilibria. For example, against a stationary opponent, these learning algorithms will converge to a best response; and, against any opponent, they will guarantee to find a strategy that does almost as well as the best possible fixed distribution of strategies against that opponent. By learning about the actual behavior of the world and of other agents, our agents will be able to predict the value of offering or accepting a deal, and so will be able to communicate with one another to select an equilibrium which participating parties predict to be mutually beneficial.

Having a unified model that can accommodate various notions of rationality (including irrationality!) is very important for this research, as illustrated by findings on how “sacred values” influence negotiating behavior. Sacred values incorporate moral beliefs that drive action in ways dissociated from prospects of success and were found by us [7,11,42] in experimental


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studies with Israelis and Palestinians to be immune to proposed tradeoffs with material or economic values.

We will investigate ways to represent cultural factors in our representation as parts of the utility function and world model of the players, e.g., as values of a multi-dimensional utility function, in which concessions on sacred values are orthogonal to material concessions; or as “shadow attributes”, i.e., attributes not under negotiation per se but entering into expected utility calculations. Additionally, we plan to study the role of sacred values in Iran, Middle East, Morocco and Turkey; study the effects of reciprocal concessions on sacred values and tradeoffs among sacred values; and develop ways to include the findings in the model.

2.4.7.1. Computational Model Framework Our model of interacting agents is designed to handle several important issues which previous

analyses have failed to address properly. First is the necessity to consider the relationship as it unfolds through time. Second is the inadequacy of traditional game-theoretic equilibrium analysis as the only tool for prediction. Third, the parties’ interests often are best described by multiple incomparable payoff measures such as financial, political, and sacred values, and these values cannot necessarily be traded for one another. Finally, it is essential to consider the long-term consequences of actions and not just their immediate outcomes. These long-term consequences can arise either through changes to the mental state of the negotiators (e.g., in their trust for one another) or through changes to the world state.

An analysis that takes these points into account allows us to put a precise basis behind well-known concepts in negotiation and in cultural views of negotiation, such as power (ability to control the outcome), accommodation (willingness to forego immediate gain to maintain the relationship), patience (trading off the cost of delaying agreement against a potentially-better outcome and against the value of information gained by waiting), value generation (the ability of the parties to come to an agreement that yields substantially more payoff to each than his safety value), and trust (belief by each party that the others intend to pursue a long-term and mutually beneficial relationship and keep their commitments). These analyses can allow us to make predictions concerning the parties’ behaviors in realistic cross-cultural situations, under different scenarios, payoff assumptions and types of uncertainty.

2.4.7.2 Interaction over time To illustrate our framework, we will start with a very simple example, and add features

incrementally. Suppose that a representative of United States forces (call him U) wishes to maintain an ongoing working relationship with the leader of a local town (call him L) as part of a mission to rebuild infrastructure and restore stability after a conflict. Over time, situations arise where one party needs the cooperation of the other: U might need information from L about the location of an insurgent leader, or L might need U’s help to rebuild or protect infrastructure such as power and water supplies. Suppose that in every interaction, each party may cooperate (C), withhold cooperation (W), or actively oppose the interests of the other party (O).

To analyze a single interaction between L and U, we could assign payoffs such as the ones given in Fig 2 and search for an equilibrium of the corresponding game. (The pentagon in the figure is the feasible region—the set of all possible joint payoffs. Its corners correspond to pure joint actions; e.g., the corner labeled WC shows the payoff to each player when U plays W and L plays C. Some corners have multiple labels, since some sets of joint actions have the same payoffs.) Note that the payoffs are asymmetric: e.g., U’s action O exacts a higher penalty from L than L’s action O does from U. The equilibria of this game are: the U. S. player always plays W (withholds cooperation), while the local leader is indifferent between C and W (attempting to


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cooperate or withholding cooperation) and can play them in any ratio. So, the one-shot equilibrium analysis predicts that the players will never cooperate.

However, this analysis is flawed in an important respect: it suggests that neither U nor L should ever be willing to help the other if helping involves incurring some nonzero cost. In actual fact, we could hope that one party would incur a small cost now in order to help the other, in the interests of maintaining a good relationship. To accurately describe the continuing relationship between U and L, we need to extend our model to include repeated interaction. 2.4.7.3 Repeated games and inadequacy of equilibrium

The simplest model of a continuing interaction is a repeated game, in which U and L repeatedly choose actions and receive payoffs according to a single-stage game. The Folk Theorem of game theory [37] tells us that within the set of possible payoffs, each participant gets at least as much in an equilibrium as he can guarantee himself by acting selfishly (called his safety value), and any possible vector of payoffs that satisfies this restriction corresponds to an equilibrium. Fig 2 illustrates these equilibria: the dashed lines indicate the safety values, and the stippled area is the set of payoffs that can result from equilibrium play.

Fig 2 demonstrates that there is more to cooperation than just equilibrium computation: the payoff vectors that correspond to equilibria (stippled region) are a significant fraction of all payoff vectors (pentagon), so neither party can use the set of equilibria to place meaningful limits on the payoffs that he will receive. Worse, each different equilibrium requires the agents to act differently, and so the set of equilibria does not give the agents any guidance about which actions to select.

2.4.7.4. Communication We propose that, instead of relying merely on computing equilibria, the parties should use

communication and learning to reach a desirable outcome. The difficulty lies in designing sound communication and learning strategies, that is, strategies which guarantee the participants that (over the long term) they will not feel an incentive to deviate, while simultaneously steering the negotiation toward a favorable outcome. Previous work by us has discovered such sound strategies for several important classes of environments. For example, our protocol [78,79] allows the parties to cooperatively compute the set of equilibria shown in Fig 2, and then negotiate to select a mutually beneficial element of the set. The protocol ensures better outcomes for both parties than does the set of all equilibria (stippled region). In sharp contrast to the traditional game theoretic analysis, our communication protocol assures a single recommended course of action, and in this recommended course the parties always cooperate with one another. (Of course, they do not blindly cooperate: if one party were to break trust, say by choosing O instead of C, then the other would react accordingly. But, Figure 2. Cooperative game


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neither party will plan to be the first to break trust.). 2.4.7.5 Learning Until this point, we have assumed that the payoffs in Fig 2 are common knowledge. In reality,

though, one player may not know the other’s payoffs. Therefore, learning may become necessary. By learning, we mean the process of estimating and adapting to both the actual (unknown) structure of the game and the strategy which the other party adopts.

Learning algorithms are not yet known for general negotiation environments. However, we have a long track record of designing learning algorithms with strong guarantees in multi-agent settings [45,54,53,103,128,129]. In particular, we will design algorithms which can, on average, come close to the incomplete-information Pareto frontier. We also propose to examine communication [59,108] and learning algorithms that take multi-attribute utility functions into account. By designing and analyzing such algorithms, we hope to explain observed negotiation strategies in cross cultural negotiations and be able to model behavior that otherwise seems irrational: for example, [42] notes that increasing the financial compensation in an offer while leaving the sacred-value-related outcome constant can actually decrease the probability of acceptance. 2.4.4.6. World State and Game Representation

A significant feature of our proposed approach is that it is capable of sophisticated reasoning about changes in the state of the world. For example, the actions of the parties could build or damage infrastructure, or affect the views of external parties (e.g., popular support). A single action could have multiple consequences: a heavy-handed raid could gather valuable information, but damage infrastructure and decrease popular support. To handle the changing state of the world, the players must plan ahead: they must reason about sequences of actions, observations, and reactions. This planning is complicated by the fact that the other players are simultaneously trying to do the same thing.

We can represent a world with changing state and partial observability as a multi-agent influence diagram, or MAID [38,58]. In a MAID, one can specify state variables to describe the world, action variables which each agent uses to influence the world, and utility variables which describe agent goals. Connections among these variables describe how they can influence one another. While MAIDs are more compact than raw extensive form games, they are limited: for example, there is no simple way to state a generalization such as “L wants all religious sites to be protected.” Instead, if there are 15 religious sites in the town (say, R1, … , R15 ), we must separately say “L wants R1 protected, L wants R2 protected,” and so forth. And, if we learn about a new site, R16 , there is no way to generalize what we know about R1, … , R15 to R16.

To address this deficiency, we need to add first-order semantics to our language. That is, we need to allow quantifiers such as “for all places x” or “there is some person y.” Doing so yields a language called multi-agent Markov logic, or MAML [45,71]. MAML generalizes both MAIDs and a representation called Markov logic networks (MLNs), a first-order probabilistic language with no direct representation of agents or goals. In MAML, we could express the rule mentioned above by writing a formula such as

( x : place) has-religious-significance(x) ∋ protected(x) ® utility(L, 10) We can read the above rule as “for each x of type place, if x has religious significance and x is

protected, then L gains 10 units of utility.” Besides hard rules (which must always be satisfied), MAML is designed to represent “soft” rules by attaching a likelihood ratio θ to each rule.

2.4.7.7 Simulator and experiments


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We are currently building (under separate funding) a simulation system in which multiple agents interact with one another and with the surrounding world. Each agent has a representation of the portion of the world it knows about (which may be quite different from the actual world due to omissions and errors). By reasoning about this representation, it can predict what other agents will do, evaluate various courses of action, and learn to make more accurate predictions and choose better courses of action. We will make this simulator available for experiments under the proposed project.

The simulator will be used in two ways: (a) for what0if analysis and prediction in machine agent interactions that embody cultural models derived from the cultural analysis; (b) human users will interact with agents that embody cultural models of interest. These two ways will be used for validation and refinement of the computational cultural models, modeling additional scenarios of interest and analysis and prediction of interventions.

2.4.8. Virtual Humans We [26,100,101,121,122,123] have developed virtual humans who can engage in both

cooperative and adversarial multi-party negotiation through face to face dialogue with people and other virtual humans. These virtual humans have a number of advanced models of mental and conversational processes, including: models of beliefs and parameterizable characteristics that can be changed according to personal or culture-specific preferences.

We plan to extend the abilities of these agents by incorporating the advanced models of culture-specific tendencies, developed in the project. We will develop dialogue strategies to cover both initiative taking and reaction to the moves of others, given the current configuration of mental models and dialogue state. These enhanced virtual humans will be used for: simulation - to see the various effects that different cultural factors may have on the outcomes and training - to allow trainees to practice different kinds of negotiation in different circumstances. 2.5. Research Evaluation

2.5.1. Data Validation The survey, interview, and experimental data to be collected by Scott Atran and other team

members in Israel, Palestine, Turkey, North Africa, and Iran will serve as the reference against which other data sources and models will be validated. The large sample Israeli/Palestinian survey will use many previously developed and validated items and a rigorous stratified sampling plan to ensure representative results. The triangulated interviews and experiments to be conducted in the other countries focus on populations of particular interest and will provide data from Jihadis, controls, and the general community that should allow identification of both cultural commonalities and features distinguishing radicalized respondents.

The email and web-based multi-player simulations are cheaper and easier to conduct and therefore will be used extensively in later stages of the project where new hypotheses have been identified and need testing. Online experimentation however lacks the control that in-country data collection allows because participants recruited online cannot be expected to accurately reflect the general population. There are also issues involving motivation and incentives and effects due to use of electronic media that could contaminate these data. We will pilot methods in the U.S. comparing participants’ behavior in face-to-face interaction vs participants recruited online to assess and try to remediate recruitment and media effects. Data from online versions of the in-country surveys will also be collected for validation and to provide indications of ways in which the recruited samples differ from their populations. The validity of data gathered from later online experiments will rest on the reliability determined by these common surveys.

2.5.2 Results cross-validation


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We will rely on a variety of procedures, including large scale interviews, surveys, and cross-cultural Internet games to gather data on cultural differences in collaboration and negotiation. We intend to obtain overlapping data for experimental hypothesis testing on the same subject populations. These data will then be used to develop cultural schema delineating prototypical goals, interpretations, and actions of members of the culture. Qualitative predictions based on these schemas can then be compared with survey/interview results and traces from online interactions to identify and correct inconsistencies. The schema could then be used as a role-playing script for further online experimentation to obtain judgments of its correctness from members of the target culture. The refined schema would then serve as the basis for composing a computational cultural model. This model could in turn be validated and refined by comparison with the original data and then run in a new online experiment. In this spiral fashion we will develop both a theory of cross cultural collaboration and negotiation embodied in the evolving schemas and a computational instantiation of that theory in the graphical models. 2.5.3. Evaluation metrics Evaluation metrics will include goal achievement outcome metrics (e.g. material gains, joint gains, relation maintenance, degree of goal fulfillment), and process measures including trust, frequency of communication and offers, reciprocity of offers, frequency of monitoring behaviors; information gathering; communication for forming shared mental models and communication for understanding someone’s interests in negotiation. 2.5.4. Training Validation In conjunction with the project advisory board and the SMEs, we will develop a set of training objectives relevant to culturally sensitive cooperation and negotiation. These objectives will be used to develop a set of training and testing scenarios for trainee interaction with virtual members of these cultures. Trainees with and without practice on the training scenarios will be asked to interact with these virtual humans in the test scenarios and rated by their instructors. We have access to military personnel at the War College, Carlisle, PA. would could participate in these experiments. Dr. Sycara’s group has worked with War College students in past projects.

2.6. Mitigation of Research Risks This research is high risk, high payoff. The challenges it faces are:

1) Data access: data collection is always a great challenge in social science research, in this project, the challenge is even greater since the research in cross-cultural and involves cultures most of which currently have animosity toward the US. The risk is mitigated here since Atran has done work before in Islamic cultures (Palestine, Indonesia) and has extensive social network connections in the cultures under study. A memorandum of understanding between our research team and the Turkish government has been agreed upon and the Moroccan government has informed us that an agreement should be ready by the end of 2007. 2) Model development: modeling cultural factors in a computational framework that will provide predictions and formal guarantees is a big challenge and one of the main goals of the research. This risk is mitigated by the spiral approach to development that continuously reconnects with data in developing schemas and then models. In addition, the cultural and military SMEs in our team will scrutinize our modeling assumptions and provide feedback. Besides the academic SMEs, Atran, Ginges, Tinsley, Dillon and military SME, LTC Grindle, we have access to Lord Alderdice an experienced international negotiator, currently active in negotiations among Sunnis and Shia in Iraq and negotiations in Kashmir. 3) Project Management: In any complex effort where multiple groups are formed to perform distinct tasks, a risk exists that each group will work separately and over time loose touch with


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the other groups. Sometimes this may lead to certain groups straying away from the core “mission” of the project and the project itself losing its sense of purpose. This also could lead to lower quality deliverables, unmet milestones, and over/under-spent budgets. We will mitigate this risk though bi-weekly teleconferences, bi-annual face to face meetings and a we site with common repository for project materials, tools, and results.

2.7. Theoretical and Scientific Significance The research will provide the following scientific advances (a) a grounded, culturally sensitive

theoretical model of dynamic intra and inter-cultural collaboration and negotiation for the cultures of interest, with analysis of similarities and differences in the different variants (and sub-groups) of the Arab cultures under study; the model will be founded on common cognitive constructs for collaboration and negotiation, thus providing theoretical integration that has been elusive thus far; (b) a novel integrated methodology involving bottom up consensus modeling and macro-structure cultural dimensions; this approach will allow the discovery of relevant cultural patterns across and within cultures, modeling of the feedback mechanism between individual cognitions and social beliefs and furnishing culturally sensitive patterns and principles for individual behaviors in dynamic collaboration and negotiation; (c) novel algorithms that extend current game theoretic models so as to allow realistic modeling of cultural factors while providing theoretical guarantees; (d) discovery of novel cultural components that influence dynamic collaboration and negotiation, such a Sacred Values

2.8. Potential Applications to Defense Missions and Requirements The research will provide validated models of cultural factors that affect dynamic collaboration

and negotiation in SSTR and other operations. These models inform training tools that will allow US military personnel to interact successfully in culturally sensitive ways in diverse situations with friendly, neutral or unfriendly actors, thus fostering cooperation and averting shifts to competitive and violent outcomes. The models will enable prediction of performance and ways to influence behaviors of interest. We will provide software artifacts in the form of multi-agent games and virtual humans for improved training in collaborative and negotiated planning.

2.9. Plans for the research training of students Seven graduate students and 2 post doctoral fellows funded by the program will actively

participate in the project’s research activities. Joint seminars at CMU and U. of Pittsburgh will be an ongoing activity throughout the duration of the project. Results of this research will be incorporated in courses taught by the participating PIs. LTC Grindle is the Director of Strategic Systems at the U.S. Army War College, Carlisle Barracks and will incorporate results of the research in course offerings there. Finally, a broader contribution to education will occur through scientific meetings, publications, and by maintaining a public project web site containing products of the research (e.g. code, data). 3. Project Schedule and Milestones

Period 1 Five months (01 May 08 to 30 Sep 08) – Design of data collection instruments for Sacred Values (SVs) and Conflict Resolution Schema (CRS) content for collaboration and negotiation; begin data collection in Middle East; begin extensions to game theoretic framework Deliverables: CRS and SV results determined thus far;. Kickoff meeting sometime within this period, date to be determined by ARO. September 08: Team meeting; discussion of data collection. .Deliverables: assessment of results thus far. MAML simulation; design of Internet cross-cultural game. Period 2: Twelve months (01 Oct 08 to 30 Sep 09) – Conclude Middle East data collection; update data, analyze; assess data collected thus far. Plan and initiate Iran experiments. Use SMEs


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for additional assessment. Study and analyze possible sources on Iraq (SME interviews, Army videos). Deliverables: Integration of findings (Middle East model) into MAML simulator and virtual human simulations; demos, reports and publications. Initial Iran results. February 2009 Team meeting, Deliverables: Report of results of the first data collection stream of interviews and surveys; revisions and plans for future assessments. September 2009 Team Meeting, Advisory Board meeting; Deliverables: report of results of second stream of data collection interviews and surveys; first generation of MAML and virtual human simulation. Period 3: Twelve months (01 Oct 09 to 30 Sep 10) – Survey and interview streams for collaboration and negotiation for CRS/SVs in Iran and Turkey; data collection and coding. Theory formation as to similar and different factors in results thus far; Integration of findings into the extended computational model(s) for Iran and Turkey into the MAML simulator and into the virtual human simulation; Deliverables: data analysis from Internet cross-cultural game with Iranian and Turkish subjects. Validation studies. Demos, reports and publications. February 2010 Team Meeting, Deliverables: report of findings on cultural factors; reports on computational model results; demos; report on new theoretical findings; revisions based on current findings. September 2010 Team Meeting, advisory board meeting; Deliverables: second generation of MAML and virtual human simulation; demo of tools to potential end users. Period 4: Seven months (01 Oct 10 to 30 Apr 11) – new theory formation based on Iran, Turkey results and cross-validation; integrate into MAML and virtual human tools; Deliverables: end to end models for Middle East, Iran, Turkey. Design of CRS Intervention studies; Demos, reports and publications. April 2011 Team Meeting, Deliverables: demo tools to potential end users; third generation MAML and virtual humans. Period 5: Five months (01 May 11 to 30 Sep 11) – Transition demos and analysis results to end users. New theory formation based on studies thus far; validation and discovery of generalizations; performance of studies to validate CRS interventions first in Turkey, then Iran; Deliverables: Design of experiments for data collection in Morocco; reports on studies thus far; September 2011 Team meeting; board meeting; Deliverables: report on analysis thus far. Period 6: Twelve months (01 Oct 11 to 30 Sep 12) – Collection of Morocco data from surveys, interviews, simulations; analysis and validation; integration (Morocco model) into MAML and virtual humans simulation; Deliverables: validation studies; analysis intervention studies for Turkey and Iran; tools and publications March 2012 Team meeting, report of results thus far from Morocco studies and intervention studies; plans for next phase; Deliverables: fourth generation MAML and virtual humans tools. Sept 2012 Team meeting; board meeting; Deliverables: demos to potential end users; Period 7: Seven months (01 Oct 12 to 30 Apr 13) – revisions to theory, models, tools based on findings; Deliverables: fifth generation MAML and virtual humans tools and results. April 2013 Team meeting; advisory board meeting; Deliverables: demos to possible end users, final report; tools and publications. 4. Assertion of Data Rights The Virtual Humans Simulation uses third party software by Epic Games; Nuance and Sonic. 5. Management Plan Our team includes US Army SME (LTC Charles Grindle, PhD candidate at the U. of

Pittsburgh, just returned from Iraq), researchers in cultural anthropology, experimental


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psychology, organizational behavior, decision science, political science, communication and linguistics and computer science. Most notably, our research team has access to the advice and guidance of Lord John Alderdice, former Speaker of the Northern Ireland Assembly and currently a member of the House of Lords. who is a renowned expert negotiator and mediator in international disputes (he led the Irish Delegation in the signing of the Belfast Agreement; in September 2007 he co-led a team of negotiators to Iraq which resulted in a public commitment by Iraq's Sunni and Shiite communities to sign off to the Helsinki Agreement and to implement its principles of non-violence and democracy). Carnegie Mellon is leading the research effort and Dr. Sycara is the lead PI.

A.Principal Investigator and other key personnel: Professor Katia Sycara of CMU is the Principal Investigator of the MURI with overall technical and managerial responsibility. Prof.. Sycara is a computer scientist with expertise in multi-agent systems, and computational models of teamwork, coordination and negotiation, and experience in leading multidisciplinary teams (e.g. she was the lead PI in an ONR MURI “Integrating Intelligent Assistants into Human Teams”). Besides her overall lead technical role, she will lead the CMU team in the areas of development of modeling cultural factors in computational game theoretic models of collaboration and negotiation that include communication and argumentation. Prof. Gordon (Department of Machine Learning, CMU), a computer scientist with expertise in machine learning and game theory will support Dr. Sycara and lead in the development and incorporation of learning into the model. Dr. Sycara will be also responsible for facilitating linkages to end users and develop potential transition strategies. Prof. Atran (Department of Psychology and Political Science, University of Michigan and John Jay College of Criminal Justice), a cultural anthropologist will lead the design of surveys interviews and other instruments, data collection, analysis and cultural theory development with particular focus on Sacred Values and emerging phenomena characterizing Islamic radical beliefs and conflict resolution attitudes. Prof. Tinsley (McDonough School of Business Georgetown University), an organizational behaviorist and anthropologist, with particular expertise in intra- and inter-cultural collaboration and negotiation, will collaborate with Dr. Atran in the development of surveys, interviews and data collection and analysis, with particular emphasis on characterizing mental schemata, how they are influenced by cultural factors and how they adapt during the course of the collaboration or negotiation. Prof. Fischoff (Department of Social and Decision Sciences, CMU), an expert in decision science and risk analysis, Prof. Dillon (McDonough School of Business, Georgetown University), an expert is management science and risk analysis and Prof. Axelrod (Departments of Political Science and Public Policy, University of Michigan), a political scientist, well known for his seminal work on the origins of collaboration, will support Dr. Atran and Dr. Tinsley in cultural analysis and theory development. Prof. Lewis (School of Information Sciences, University of Pittsburgh), an experimental psychologist will lead the experimental design, development of inter-cultural process simulations, and support Drs. Atran and Tinsley in data analysis. Prof. Traum (Dept of Computer Science, University of Southern California), a computational linguist, will be responsible for development of virtual humans for scenario simulation and military training. LTC Grindle will serve as SME.

B. Subawards: Planned sub awards are to the University of Pittsburgh, Georgetown University, John Jay College of Criminal Justice, University of Southern California. Prof. Axelrod of University of Michigan will act as a consultant and will be included in the budget of CUNY. Lord Alderdice has graciously volunteered to provide advice without compensation. A small amount for reimbursing his research related travel (e.g. team meetings) is part of the


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CUNY budget. The complementary research expertise of our team, individually and collectively in cultural anthropology, psychology, organizational behavior, decision sciences and computer science as well as the long experience of our team in anthropology field work, theory and models building and evaluation fully support the objectives of the MURI. All team members have been extremely successful in transitioning research results to DoD applications (for details, see CVs). Govt and other related collaborations CMU has ongoing collaborations with ARL, AFRL/MN where tools have been transitioned; USC has long and successful involvement with ARO-funded ICT tool development for training activities. The SMEs and advisory board will provide additional suggestions and guidance on transition paths, e.g. Human Terrain Assessment.

C. Ongoing and pending research projects: Sycara (PI) 11/01/06-10/31/09 .20 CY AFOSR $1,158,749. Title: Large Scale Distributed Computation for Team Coordination in Dynamic Environments. Relation: Algorithms for information propagation in multi-agent distributed sensor networks. Sycara (PI) 05/12/06-05/11/11 .10 CY IBM subcontract to CMU ARL $860,000. Title: Wireless Networking & System Security in Coalition Settings. Relation: Agent support for military teams. Sycara (PI) 05/10/06-07/26/2010 .20 CY BBN subcontract to CMU DARPA. $2,735,934 Title: Integrated Learning. Relation: Algorithms for agent learning. Atran (PI) 4/07-3/10 .08 CY MIT subaward to CUNY; Title; Computational Modeling of Adversary Attitudes and Behaviors”; Relation: adversary models Atran: (PI) .15 CY 6/06-5/09 AFOSR $1.5M “Small Group Dynamics in the Evolution of Global Network Terrorism Relation: Assessing SVs and their relation to terrorism Dillon (co-PI) 07/30/2006 – 07/30/2008 .10 CY NSF $299,575 Title: Correctly Interpreting Near-Miss Events for Hurricanes; Relation: Risk analysis Dillon: (co-PI) 07/30/2006 – 03/30/2008 .10 CY NASA $149,998 Title: Interpreting Precursor Events: A Prescriptive Risk-Based Approach to Preventing Future Mission Catastrophes; Relation: Risk analysis Dillon: 09/30/2004 – 03/30/2008 .10 CY NSF $85,000 Title: Investigating Alternative Decision Models for Earthquake Preparedness. Relation: Risk analysis Fischhoff 10/01/2007-9/30/2008 .08 CY DoD $250,000 Title: An Integrated Risk Assessment of Radicalization. Relation: Risk assessment of conflict personal preferences. Fischhoff: 1/1/07 – 12/31/09 .05 CY Commonwealth of PA. $649,424 Title: Health Research Formula Fund 2006-07. Relation: none. Fischhoff: 2/1/07 – 1/31/08 .08 CY Federal Reserve Bank of New York $227,315 Title: A Mental Models Approach to Developing a Valid Survey of Inflation Beliefs Relation: none Gordon (PI) 4/07-4/08 .45CY DARPA $9.5M Computer Science Study; Relation: game theoretic models. Gordon: 10/07/-9/08 .2 CY SRI subcontract to CMU DARPA $9.8M Title: RADAR: Reflective Agents with Distributed Adaptive Reasoning; Relation: learning tasks by intelligent assistant Gordon 9/03-9/08 .3 CY NSF $2.9M Title: Next Generation Bio-Molecular Imaging and Information Discovery; Relation: learning from images.


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Lewis (co-PI) 11/01/06-10/31/09 .40 CY AFOSR $$501,277. Title: Large Scale Distributed Computation for Team Coordination in Dynamic Environments. Relation: Algorithms Tasking and interacting with distributed autonomous agent networks Lewis: 05/06-04/09 .08CY UCF subcontract ONR: $482,886 Title: Systems for Understanding and Measuring Macrocognition in Teams Relation: interaction of humans and automation Tinsley (co-PI) 07/30/2006 – 07/30/2008 .10 CY NSF $299,575 Title: Correctly Interpreting Near-Miss Events for Hurricanes; Relation: Risk analysis Tinsley (co-PI) 07/30/2006 – 03/30/2008 .10 CY NASA $149,998 Title: Interpreting Precursor Events: A Prescriptive Risk-Based Approach to Preventing Future Mission Catastrophes; Relation: Risk analysis Traum 11/1/06-10/31/07 .67 CY Contract Sponsor: ARO $791,824 Title: Virtual Humans: ICT Natural Language Relation: Developing basic communication and negotiating agent infrastructure that will be extended in this project. Traum: 6/1/07-12/31/07 .10 CY Contract ARO: $444,309 Sgt. Star Immersive Demonstration Sponsor: ARO Relation: none. Traum: 5/10/07-5/11/08 .25 CY Contract ARO: $821,632 Tactical Questioning for the US Army Sponsor: ARO Relation: Developing language processing technology that may be extended in this project. Traum: 8/15/07-8/31/08 .10 CY Contract ARO: $263,699 Joint Fires and Effects Trainer System –Enhanced 2007 Sponsor: ARO Relation: none. Traum: 11/1/08-9/30/09 .50 CY Contract Sponsor ARO: $1,666,267 Title: Virtual Humans: ICT Natural Language Relation: Developing basic communication and negotiating agent infrastructure that will be leveraged and extended in this project. Traum: 11/1/07-10/31/08 .05 CY Contract Sponsor ARO: $100,000 Title: New Tools for Rapid NLU and NLG Development Relation: none. Traum: 11/1/07-10/31/08 .05 CY Contract Sponsor ARO: $102,600 Title: Cross cultural perceptions of human and virtual human group conversational behavior Relation: studying communication behavior of people from different cultures. May provide data to analyze for this project, or theories that can be tested with new cultures.

D. Facilities The research team has extensive facilities and experience to perform the proposed research. Each of the universities involved in the project have state of the art computing facilities available to all faculty and students, as well as facilities for running experiments involving humans.. Each campus has high speed LANs throughout and extensive wireless access. The University of Pittsburgh also has a super-computing center for very computationally complex applications. The budgeted equipment will consist of servers for data and software storage at CMU and also for laptops for research work and demonstration of software tools.

E. Management Interaction: Dr. Sycara will be assisted by a MURI Science Advisory Board consisting of key scientific and DoD stakeholders. Dr. Thomas Killion, Deputy Assistant Secretary for R&T, and Gen. Garrett Senior Science Advisor for Army G2 & INSCOM have already agreed to serve on the Board. The Board will provide guidance and facilitate linkages for potential transitions. We will use a program management website, hosted by CMU, the technical results portion of which will include all papers, tools and results and portions of which we will make publicly available as appropriate. Members of the team, e.g. Atran, Fischoff, Axelrod, also Sycara, Lewis and Gordon, have a history of working together. To enhance collaboration and


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cross-fertilization of research, we will have joint technical meetings and joint demonstrations. The initial joint meeting will be held shortly after contract award. Additionally, we will hold bi-annual face to face meetings, including university PIs, students and staff, rotating the location among the participating institutions. In these meetings, the participants will report research results and plan research activities for the next six months. In the interim, research activities will rely on regular e-mail exchanges and bi-monthly teleconferences. Additional visits will be arranged as needed. Collaborative efforts in publishing and transitioning the research results will be undertaken as well as frequent joint demonstrations. In addition, the PIs will integrate research results, tools and models in courses they teach at their host institutions as appropriate.

G. Other submissions of this proposal: None


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