Most of the work in agent-based social simulation has assumed highly simplified agent models, with little attention being paid to the details of individual cognition. Here, in an effort to counteract that trend, we substitute a realistic cognitive agent model (CLARION) for the simpler models previously used in an organizational design task. On that basis, an exploration is made of the interaction between the cognitive parameters that govern individual agents, the placement of agents in different organizational structures, and the performance of the organization. It is suggested that the two disciplines, cognitive modeling and social simulation, which have so far been pursued in relative isolation from each other, can be profitably integrated.

The models used in social simulation to date have mostly been very simplistic cognitively, with little attention paid to the details of individual cognition. This work proposes a more cognitively realistic approach to social simulation. It begins with a model created by Gilbert (1997) for capturing the growth of academic science. Gilbert’s model, which was equation-based, is replaced here by an agent-based model, with the cognitive architecture CLARION providing greater cognitive realism. Using this cognitive agent model, results comparable to previous simulations and to human data are obtained. It is found that while different cognitive settings may affect the aggregate number of scientific articles produced, they do not generally lead to different distributions of number of articles per author. The paper concludes with a discussion of the correspondence between our model and the constructivist view of academic science. It is argued that using more cognitively realistic models in simulations may lead to novel insights.

This paper describes a spatially-explicit agent-based model of river basin land use and water management. The model is being implemented within a project aimed at investigating ways of synthesising stakeholder priorities, taking the EU Water Framework Directive as a case study. There are many human activities that take place in a river basin and can alter the ecological status of the water, and there are also many activities whose outcomes depend on that ecological status. These interactions between the socio-economic and the ecological aspects of the river basin are shaped by the spatial distribution of the situation. Water users upstream generally have an advantage over those downstream: the first chance to use (and perhaps abstract or pollute) the water. The flowing nature of water creates asymmetries in the interactions between users. FEARLUS-W is a spatiallyexplicit agent-based model built to increase our understanding of these complex interactions and explore how common-pool resource problems in river basin management might be tamed through socio-economic interactions between stakeholders (primarily rural land managers), and through management strategies aimed at shaping these interactions. FEARLUS-W is being constructed within an extended version of an existing spatiallyexplicit agent-based model of land use change, FEARLUS (Polhill, Gotts and Law, 2001), drawing on theories of common-pool resource use, and on survey work among stakeholders. The main extensions to FEARLUS deal with water, water flow and water pollution on the one hand and allow for agents with multiple and potentially conflicting top-level goals on the other.

This article explores the view that computational models of cognition may constitute valid theories of cognition, often in the full sense of the term ‘‘theory”. In this discussion, this article examines various (existent or possible) positions on this issue and argues in favor of the view above. It also connects this issue with a number of other relevant issues, such as the general relationship between theory and data, the validation of models, and the practical benefits of computational modeling. All the discussions point to the position that computational cognitive models can be true theories of cognition.

Although computational models of cognitive agents that incorpo-rate a wide range of cognitive functionalities have been developed in cognitive science, most of the work in social simulation still assumes rudimentary cognition on the part of the agents. In contrast, in this work, the interaction of cognition and social structures/processes is ex-plored, through simulating survival strategies of tribal societies. The results of the simulation demonstrate interactions between cognitive and social factors. For example, we show that cognitive capabilities and tendencies may be relevant to what social institutions may be adopted. This work points to a cognitively based approach towards social simu-lation, as well as a new area of research —exploring the cognitive-social interaction through cognitively based social simulation.

this paper, uniquely for the present collection, the agents are representing inanimate objects (grains of sand) rather than animate (usually human) creatures. The paper by Kafeza and Karlapalem investigates and extends the work flow management system framework to speed up activities in multi agent environments, using simulations to assess the effectiveness of their innovation. While this paper is the least clearly focused on agents, it is also the clearest in raising implementation issues that affect agent and mechanism design.

Abstract — This work describes a cognitively realistic approach to social simulation. It begins with a model created by Gilbert [4] for capturing the growth of academic science. Gilbert’s model, which was equation-based, is replaced here by an agent-based (neural network) model, with the (neural network based) cognitive architecture CLARION providing greater cognitive realism. Using this agent model, results comparable to previous simulations and to human data are obtained. It is found that while different cognitive settings may affect the aggregate number of scientific articles produced by the model, they do not generally lead to different distributions of number of articles per author. It is argued that using more cognitively realistic models in simulations may lead to novel insights. I.

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Productions functions map the inputs of a firm or a productive system onto its outputs. This article expounds generalizations of the production function that include state variables, organizational structures and increasing returns to scale. These extensions are needed in order to explain the regularities of the empirical distributions of some economic variables.