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Computer simulation and organizational studies

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Introduction

Computer Simulation has been a prominant method in organizational studies and strategic management. While their are many uses for computer simulation (including the development of engineering systems inside high-technology firms), most academics in the fields of strategic management and organizational studies have used computer simulation about how organizations or firms work. While the strategy researchers have tended to focus on testing theories of firm performance and many organizational theorists are focused on more descriptive theories, the one uniting theme has been the use of computational models to either verify or extend theories. It is perhaps no accident that those researchers using computational simulation have been inspired by ideas from biological modeling, ecology, theoretical physics and thermodynamics, chaos theory, complexity theory and organizational studies since these methods have also been fruitfully used in those areas.

Basic Distinctions

Those studying organzations and firms using computer simulations mayneed to understand a varity of basic distinctions and definitions that are used in by computational scientists

  • Agent-based vs Equation-based: agent-based models unfold according to the interactions of relatively simple actions, while equation-based models unfold numerically based on a variety of dynamic equations (Note: some aruge this is something of a false distinction since some agent based models use equiations to direct the behavior of their agents)
  • Model: simplied versions of the real world that contain only essential elements of theoretical interst (Lave and March)
  • Complexity of the model: the number of conceptual parts in the model and the connections between those parts (Simon)
  • Deterministic vs. Stochastic: deterministic models unfold exactly as specified by some pre-specified logic, while stochastic models depend on a variety of draws from probability distributions
  • Optimizing vs. Descriptive: models with actors that either seek optimums (like the peaks in fitness landscapes) or do not

Methodological Approaches

There are a variety of different methodological approaches in the area of computational simulation. These include but are not limited to the following. (Note: this list is not Mutually Exclusive nor Collectively Exhaustive, but tries to be fair to the dominant trends. For three different taxonomies see Carley 2001; Davis et. al. 2005; Dooley 2002)

  • Agent-Based Models: computational models investigating the interaction of multiple agents (many of the following approaches can be 'agent-based' as well)
  • Cellular Automata: models exploring multiple actors in physical space whose behavior is based on rules
  • Genetic Algorithms: models of agents whose genetic information can evolve over time
  • Equation-Based (or Non-Linear Modeling): models using (typically non-linear) equations that determine the future state of its sytems
  • Social Network models: any model representing actors as connected through stereotypical 'ties' as in social network analysis
  • Stochastic Simulation: models that involve random variables or source of stochasticity
  • Systems Dynamics: equation-based approach using casual-loops and stocks & flows of reources
  • NK Modeling: actors modeled as N nodes linked through K connections that are (typically) trying to reach the peak of a fitness landscape

Early Research

Early research in strategy and organizations using computational simulation concerned itself with either the macro-behavior of systems or specific organziational mechanisms. Highlights of early research included:

  • Cohen, March,& Olsen's 1972 Garbage Can Model of Organizational Choice modeled organizations as a set of solutions seeking problems in a rather anarchic 'garbage can'-esque organization.
  • March's 1991 study of Exploration and Exploitation in Organizational Learning utilized John Holland's basic distiction to show the value of slow learners in organizations.
  • Nelson & Winter's 1981 "Evolutionary theory" used a simulation to show that an evolutionary model could produce the same sort of GDP / productivity numbers as neo-classical rational choice theorizing.


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