In This Article Computational Modeling

  • Introduction
  • Journals

Management Computational Modeling
Jeffrey B. Vancouver, Xiaofei Li
  • LAST MODIFIED: 28 March 2018
  • DOI: 10.1093/obo/9780199846740-0135


Computational models are formal representations of theory or phenomena that can be simulated over time or some other dimension (e.g., options, choices, or events). Formal theories are distinct from verbal or natural language theories, which is the standard method of theory description in management and psychology. In the field of management, computational models have primarily been used to represent processes within organizations, industries, or markets at the macro level of analysis. Micro-level computational models of individuals working in organizations or working on tasks or problems one might encounter in organizations were common when computational modeling first emerged in the late 1950s and have made a resurgence in recent years (e.g., “A Formal, Computational Theory of Multiple-Goal Pursuit: Integrating Goal-Choice and Goal-Striving Processes”). Meso-level models represent units (e.g., individuals, teams) as agents nested in contexts. Computational models have several advantages over the more commonly used natural language (i.e., verbal) theory, including precision and transparency of description, logical consistency, and the ability to use the representation of the theory to determine via simulation the implications of the theory across time or whatever dimension is considered. Moreover, elements and parameters in the model can be systematically varied as a method for theory testing. This latter quality can be especially useful when the units of study or constructs are difficult to manipulate or examine empirically. Another type of formal model is mathematical. Unlike computational models where the outcomes are determined computationally through simulations, in mathematical models researchers aim to use mathematical proofs to derive theories from a set of mathematical conditions that describe the phenomenon of interest (“The Case for Formal Theory”). However, when the phenomenon of interest is complex, obtaining closed form statements through mathematical proof could be very challenging or even intractable. Although it is possible to obtain more tractable models by simplifying reality, the price of the lost accuracy could be too dear. On the other hand, algorithms implemented in computational simulation models can easily overcome such issues (how to build and use agent-based models in social science; computer simulation: the third symbol system). Both mathematical and computational models are considered formal models, the primary form of theory in many of the physical sciences (e.g., physics) and some of the social sciences (e.g., cognitive psychology), but they are relatively rare in management science. Nonetheless, there are exceptions. For example, prospect theory (prospect theory: an analysis of decision under risk) is a well-known mathematical theory of decision making. Likewise, broader computational architectures, like game theory, system dynamics, control theory, and formal propositions theories like SOAR and ACT-R are well-known and pervasive.


Books related to computational modeling tend to come in three forms: instructional texts, edited volumes, and theory-based. Instructional texts can be used for courses or as references for creating and evaluating models. Generally, these textbooks will only be appropriate for graduate courses given the level of mathematical exposure required. Edited volumes contain chapters by different contributors and thus provide a good way to see the breadth of use of computational modeling as a tool for scholarship or practice. Introductory chapters advocate for and explain computational modeling as a scientific or practical tool and most chapters describe a single computational model or set of models related to a single phenomenon or a specific technique for model assessment. Theory-based books present models based on a particular architecture, which is a meta-theoretical framework for building more specific models. These latter types are listed in the topic sections in which they are most relevant.

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