Agent-Based Computational Modeling

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In short form, some of my epistemological arguments favoring the use of heterogeneous agent models are as follows. Needless to say, the papers elaborate the points made here, especially #s 9 and 8 or 10.

  1. Model-Centered Science. Toward the end of the 20th century, philosophers moved away from positivism to adopt a more probabilistic view of truth statements. Campbell’s contribution is to recognize that real-world phenomena may act as external criterion variables against which theories may be tested without social scientists having to reject individual interpretationist tendencies and social construction. Models are the central feature of the Semantic Conception--it bifurcates scientific activity into tests of the theory–model link and the model–phenomena link. In this view, theory papers should end with a (preferably) formalized model and empirical papers should start by aiming to test the ontological adequacy of a model. Most social science papers are not so oriented.
  2. Math Molding Effects. The message from the Morgan and Morrison chapters speaks to the autonomous influence of math models on science. Read points to the fundamental molding effect of math models on social science. He points to their fundamental limitation, “…A major challenge facing effective—mathematical—modeling…is to develop models that can take into account…[agents’] capacity for self-modification according to internally constructed and defined goals.” Basically, the assumptions required for tractable mathematics steer models away from the most important aspects of human behavior. Because of this, mathematics is (mostly) bad for social science! To the extent that there are formal models in social science they tend to be math models—a clear implication to be drawn from Henrickson’s citation survey. Few social scientists use models immune to the molding effects of the math model.
  3. Postmodernism’s Connectionist Core. We do need to give relativists and postmodernists credit for reminding us that “We ARE the Brownian Motion!” Most natural scientists are separated from their “agents” by vast size or distance barriers. Social scientists are agents doing their science right at the agent level. Most sciences do not have this luxury. But it also means a fundamental difference. We are face to face with stochastic heterogeneous agents and their interconnections. Social scientists should want a scientific modeling epistemology designed for studying bottom-up order-creation by agents. Unfortunately, many postmodernists base their anti-science rhetoric on an abandoned epistemology and ignore a “new” normal science ontological view very much parallel to its own. As Cilliers argues, postmodernism zeros in on the web of interconnections among agents that give rise to localized scientific textual meanings. In fact, its ontology parallels, and gains legitimacy from, that of complexity scientists. The lesson from complexity science is that natural scientists have begun finding ways to practice normal science without assuming away the activities of heterogeneous autonomous agents. There is no reason, now, why social scientists cannot combine “new” normal science epistemology with postmodernist ontology. Yet very few have done so.
  4. Legitimacy. Given the connectionist parallels between complexity science and postmodernist views of human agents, we conclude that their ontological views are isomorphic. Complexity science ontology has emerged from foundations in classical and quantum physics and biology. Postmodernist ontology has emerged from an analysis of the human condition. Thus, an epistemology based on complexity science and its agent-based modeling approaches may be applied to social science ontology as reflected in the agent-based ontology of postmodernism. “New” social science also draws legitimacy from other sources:
    • Campbellian realism, coupled with the model-centered science of the Semantic Conception, bases scientific legitimacy on theories aimed at explaining transcendental causal mechanisms or processes, the insertion of models as an essential element of epistemology, and the use of real-world phenomena as the criterion variables leading to a winnowing out of less plausible social constructions.
    • The core of postmodernism sets forth an ontology that emphasizes meanings based on the changing interconnections among autonomous, heterogeneous social agents—this connectionist-based, social agent-based ontology offers social science a second basis of improved legitimacy.
    • The “new” normal science emerging from complexity science has developed an agent- and model-centered epistemology that couples with the ontological legitimacy from postmodernism. This offers a third basis of improved legitimacy.
    • Model-centered science is a two edged sword. On the one hand, formalized models are reaffirmed as a critical element in the already legitimate sciences and receive added legitimacy from the Semantic Conception in philosophy of science. On the other, the more we learn about models as autonomous agents—that offer a third influence on the course of science, in addition to theory and data—the more we see the problematic molding effects math models have on social science. In short, math models are inconsistent with the new agent- and model-centered epistemology as they require assuming away both the core postmodernist ontology and “new” normal science ontology. Thus, alternative formal modeling approaches—such as agent-based modeling—gain credibility. This offers a fourth basis of improved legitimacy.
    • In a classic paper, Cronbach divided research into two essential technologies: experiments and correlations. Since then we have added math modeling. As Henrickson’s journal survey shows, nonlinear computational models are rapidly on the increase in the natural sciences. Rounding out the social scientists research tool bag and finding a technology that fits well with social phenomena surely adds a fifth basis of improved legitimacy.

Agent-based models offer a platform for model-centered science that rests on the five legitimacy bases described in the previous points: Agent-based models support a model-centered social science that rests on strongly legitimated connectionist, autonomous, heterogeneous agent-based ontology and epistemology. Yet very few social scientists have connected the use of agent-based models with the five bases of legitimacy.

Paper # 11 concludes with my observation that agent-based modeling should emerge as the preferred modeling approach. Future, significant, social science contributions will emerge more quickly if science-based beliefs are based the joint results of both agent-modeling and subsequent empirical corroboration. Each of the five bases of legitimacy that support our use of the word, should, rest on solid ontological and epistemological arguments where “analytical adequacy” builds from agent-based models and “ontological adequacy” builds from the presumption that social behavior results from the interactions of heterogeneous agents. Here is where agent-based models have much to offer. They allow us to accomplish the following objectives:

  1. Formal modeling without having to assume away the essential character of postpositivist ontology: complexity, diversity, heterarchy (multiple orders and constraints), vast networks of connections, indeterminate social behaviors, mutual causality, and so forth, nor the three other Aristotelian causes in addition to efficient cause: material, final, and formal.
  2. Extracting more plausibly true, potentially generalizable, and predictable theories from complicated narratives bound to a particular locality, context, and time.
  3. Shifting from Mohr’s variance theory and efficient cause to his process theories and including material, formal, and final causes, along with the study of the complex interactions of the four different kinds of causality.
  4. Researching multifinality and equifinality and their relation to the three causes in addition to efficient cause.
  5. Reducing initially complicated theories about a complex world to agent rules, in abstracted, idealized, agent-based model form, so as to study and model how agent rules lead to order creation and the formation of norms, hierarchy, institutional structure, supervenience, and the like.
  6. Seeing whether the analytical truth plausibility of theories may be improved by testing which of the various proposed elements of the theories work best in producing outcomes predicted by the theories, thereby leading toward the production of more elegant theories composed of fewer, but more fruitful, elements.
  7. Aiming for theories that have more empirical truth plausibility because they (a) more adequately represent the state-space of real-world firms; and (b) have been tested against real-world phenomena.
  8. Forcing elegance on theories by the use of models offers simpler, theory-based, more plausibly true beliefs, and increasingly crystallized, more easily described messages for management researchers to take to practicing managers.

Taken all together, these eight objectives provide a means of building from the richness of postmodernist and process theory narratives toward more elegant theories and more plausibly true theory-based beliefs about how to bring the four Aristotelian causes to bear in improving management practice.