simulation models as a research method
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Simulation Models as a Research Method. Professor Alexander Settles. Research Methodology - Simulation. Simulation as a research tool Research in simulation Focus here is on simulation of discrete event dynamic systems. Social Simulation. - PowerPoint PPT PresentationTRANSCRIPT
Simulation Models as a Research Method
Professor Alexander Settles
Research Methodology - Simulation
• Simulation as a research tool
• Research in simulation
• Focus here is on simulation of discrete event dynamic systems
Social Simulation• Most social science research uses some kind of theory or model
– Theories are generally stated in textual form
– But some are represented as equations
– Sometimes carry out experiments on artificial social systems that would be impossible or unethical to perform on human populations
• One advantage: must think through your assumptions
– Clarity and precision; each parameter needs a value
– All the detail of the model can be inspected by others
• Disadvantage: data adequate for estimating all parameter values may be hard to get
Sociology and Complexity• The physical world is full of systems that are (almost) linear
• But (human) societies have quite unpredictable features
– Their characteristics at any one time are affected by their past histories (‘path dependence’)
– E.g., adoption of 1 of a pair of alternative technologies by a society can be greatly influenced by minor contingencies about who chooses which technology early on
• Human societies, institutions and organizations are complex systems
– The behavior of the system as a whole can’t be understood in terms of the separate behaviors of its parts
– Contrasts with reductionist physical sciences
Simulation as a Research Tool
• Why simulation?– An analytical approximation has been developed
to model some system performance measure.– The development of the approximation requires
simplifying assumptions/approximations.– The conjecture is that the analytical model is still a
reasonable representation of the real system.– Simulation is being used to support or refute this
conjecture.
Simulation as a Research Tool
• Are the assumptions applied in the simulation clearly stated?– Distributions used.– Operational protocols, e.g., blocking, etc.– Correlation? – Can you simulate the same system?
• Steady State vs. Terminating – Number of runs – Length of runs
• Some models take a long time to “settle down”
Simulation as a Research Tool
• Verification & validation– Mainly applies to studying a real system or a
detailed representation– How was this conducted?
• Results compared to an existing system?• Comparisons made to existing analytical results?• Extreme cases tested?
Simulation as a Research Tool
• Experimental design– Experimental design?– Random systems?
• The importance of this depends on the way the simulation was used– If simulating to understand a system and
gain insight, these issues become more important
Methods of simulation
• System dynamics– Behavior of a system with complex causality
and timing– System of intersecting, circular causal loops– Stocks that accumulate and dissipate over
time– Flows that specify rates within system– Inputs to a system of interconnected causal
loops, stocks, and flows produce system outcomes
System Dynamics Research Tools
• Add causal loops• Change mean of flow
rates• Change variance of
flow rates
System Dynamics Research Questions
• How do organizations undergo fundamental change?
• When do small interruptions create major catastrophes?
• What conditions create system instability?
NK fitness landscapes
• Speed and effectiveness of adaptation of modular systems with tight versus loose coupling to an optimal point
• System of N nodes, K coupling between nodes
• Fitness landscape that maps performance of all combinations
NK Fitness landscape
• (S, V, f) :• S: set of admissible
solutions,• V : S → 2S
function, :neighborhood
• S → IR: fitness function.
Key Assumptions
• Adaptation via incremental moves and long jumps
• Optimization
• Adaptation of a modular system using search strategies (i.e., long jumps, incremental moves) to find an optimal point on a fitness landscape
NK fitness landscapes
• Vary N and K
• Change adaptation moves
• Add a “map” of the landscape
• Create an environmental jolt
NK fitness landscapes
• How long does it take to find an optimal point (e.g., high-performing strategy)?
• What is the performance of the optimal point?
• What is the optimal strategic complexity?
• How does cognition improve experiential learning?
Genetic algorithms
• Adaptation of a population of agents (e.g., organizations) via simple learning to an optimal agent form
Genetic algorithms
• Adaptation of a population of agents (e.g., organizations) via simple learning to an optimal agent form
• Population of agents with genes• Evolutionary adaptation (v-s-r) • Variation via mutation (mistakes) and
crossover (recombination)• Selection via fitness (performance)• Retention via copying selected agents
Theoretical Logic
• Optimization
• Adaptation of a population of agents using an evolutionary process toward an optimal agent form
Research Questions
• How does adaptive learning occur within bargaining?
• How does organizational learning affect the evolution of a population of organizations?
• What affects the rate of adaptation (or learning or change)?
• When and/or does an optimal form emerge?
Genetic algorithms
Cellular automata
• Emergence of macro patterns from micro interactions via spatial processes (e.g., competition, diffusion) in a population of agents
Cellular automata
• Population of spatially arrayed and semi-intelligent agents
• Agents use rules (local and global) for interaction, some based on spatial processes
• Neighborhood of agents where local rules apply
Research Questions
• How does the pattern emerge and change?
• How fast does a pattern emerge?
• How do competition and legitimation affect density dependence?
Stochastic processes
• One or more processes by which system operates
• One or more stochastic sources (e.g., process elements)
• Probablistic distributions for each stochastic source
Definition
• A stochastic process is one whose behavior is non-deterministic in that a system's subsequent state is determined both by the process's predictable actions and by a random element.
• Manufacturing process
• Finance – asset pricing – Markov chain
Research Questions
• What is the relationship between exploration and exploitation?
• What is the optimal degree of structure?