Computer simulation and organizational studies

Computer simulation is a prominent method in organizational studies and strategic management. While there 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 to understand how organizations or firms operate. More recently, however, researchers have also started to apply computer simulation to understand organizational behaviour at a more micro-level, focusing on individual and interpersonal cognition and behavior such as team working.

While the strategy researchers have tended to focus on testing theories of firm performance, 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 organization studies since these methods have also been fruitfully used in those areas.

Basic distinctions/definitions

Researchers studying organizations and firms using computer simulations utilize a variety of basic distinctions and definitions that are common in computational science

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 or steady-state equations (Note: some argue this is something of a false distinction since some agent based models use equations to direct the behavior of their agents)

Model: simplified versions of the real world that contain only essential elements of theoretical interest

Complexity of the model: the number of conceptual parts in the model and the connections between those parts

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.