ABS Key Assumptions Agents interact with little or no central authority or

Abs key assumptions agents interact with little or no

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ABS: Key Assumptions
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Agents interact with little or no central authority or direction: Global patterns emerge from the bottom-up "Self-organization" process (Kaufmann 1996) Agents are interdependent: Agents influence others in response to influence they receive. Agents follow simple rules: Global complexity does not necessarily reflect the cognitive complexity of individuals Simon contended that human beings are quite simple. “the apparent complexity of our behavior is largely a reflection of the complexity of the environment.” (Simon 1998) Agents are adaptive and backward-looking. When interdependent agents are also adaptive, their interaction can generate a “complex adaptive system” (Holland 1995) Agents adapt at two levels: individual and population (or group) Individual learning alters the probability distribution of rules competing for attention. Population learning alters the frequency distribution of agents competing for reproduction through processes of selection, imitation, and social influence. ABS Characteristics The observed behavior is typically best described qualitatively and the reasons individuals give for their behavior is almost invariably qualitative as well. Validation of software agents as good representations of real individuals can be facilitated by: having the agents perceive events specified by qualitative descriptions maintaining the qualitative terms in processing those perceptions acting in ways that can be described qualitatively A natural way to maintain this qualitative link between the language of actors and the language of the agents is to use production systems whereby: the rule conditions describe the perceptions by the agents processing is governed by some inference engine the actions are specified by the consequents of the rules There are many logics that can facilitate this representation, but few are known to social scientists. ABM/ABS Benefits - I
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Address fundamental problems seen in many disciplines: Path dependency Adaptive vs. Rational Behavior Consequences of heterogeneous agents Effects of network structure Diffusion of innovation Cooperation among egoists Leads to cross-fertilization among multiple disciplines as we recognize similarity of problems and solutions. Knowledge-based models Knowledge-based simulations models are computational systems for processing information based on beliefs, goals, and interactions that produce behaviors or consequences. Types of knowledge-based models: semantic networks frame systems, and rule-based systems Social science examples are numerous, including expert-based systems, and include: STRADS (illustrated below) POLI (Taber, 1992) STRADS Strategic Automated Discovery System Basic Idea: Given an event, STRADS simulates the responses of various actors (countries, leaders, NGOs, etc.) in the context specified by the particular scenario.
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  • Spring '15
  • StephenKaisler

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