Topics in Cognitive Science 6 (2014) 148–149 Copyright © 2013 Cognitive Science Society, Inc. All rights reserved. ISSN:1756-8757 print / 1756-8765 online DOI: 10.1111/tops.12056

Modeling Collaborative Coordination Requires Anthropological Insights Jeremy Karnowski Department of Cognitive Science, University of California, San Diego

Artificial Intelligence highly influenced Cognitive Science in its infancy, as researchers in Computer Science and Cognitive Psychology created a metaphorical link between software on programmable digital computers and human cognition occurring in the brain, allowing them to “see inside the black box.” Marr (1982), through his work on vision, further extended this information processing metaphor by outlining three complementary levels of analysis:

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Computational—What is the problem the system is trying to solve? Algorithmic—What are the representations and manipulations used to solve the problem? Implementational—How is the solution physically realized?

Current computational accounts continue to focus on the individual. Humans, however, are not solitary creatures and perhaps were selected for their ability to be part of a collaborative collective (Tomasello et al., 2005). Therefore, to explore facets of human cognition, it might be advantageous for us to consider computational-level accounts of problems which are faced by a system of multiple agents and how current human coalitions solve these problems. Anthropology, as a group that is interested in content and shared representations, provides a critical role in helping establish these computational accounts. Fieldwork is vital to documenting the types of problems facing groups of living humans and establishing the multifaceted patterns of behavior which represent working attempts at a solution. For instance, Hutchins (1995) investigates the problem of collaborative navigation (computational level), the different ways that Western and Micronesian cultures solve this problem (algorithmic level), and how these solutions were accomplished through different sets of cognitive and cultural artifacts (implementational level). Anthropological data are invaluable because Correspondence should be sent to Jeremy Karnowski, Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jollla, CA 92093-0515. E-mail: [email protected]

J. Karnowski / Topics in Cognitive Science 6 (2014)

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even though solutions to computational problems are often intractable, groups of humans continue to succeed where powerful computer algorithms fail. There is an opportunity for the computationally inclined to determine how existing human solutions fit into their formal framework, which would not only promote the identification of novel policies but also help researchers determine properties of successful policies in general. There currently exist computational techniques that allow us to describe and provide a formal treatment of the dynamics of multi-agent behavior that might be appealing to both Cognitive Science and Anthropology. Decentralized partially observable Markov decision processes (Dec-POMDPs) (Bernstein, Givan, Immerman, & Zilberstein, 2002) is one such framework, and it allows researchers to formalize scenarios in which a group of agents attempt to maximize a behavioral objective while confronted with sensor and motor uncertainty. A Dec-POMDP solution is a set of individualized action policies which in turn leads to successful collaborative action. If all agents are identical, one only need solve for a single policy which is then utilized by each agent. All agents in real life, however, are not made alike and solving decentralized problems is more than just understanding individual cognition in the midst of others. An individual’s history of sensorimotor engagement with the world alters the internal organization of information (Lungarella & Sporns, 2005) which leads to differences in perception (set of possible hypotheses about the world given data). Dec-POMDPs can incorporate this feature and provide us with a way to model these realistic scenarios where, even though the cognitive processes (probabilistic distributions and inference mechanisms) are invariant across individual agents, it is more than just the content (the sampled data) which is variable. Incorporating knowledge distributions expands the space of problem solutions; restricting agents to be identical, while it focuses on the individual, may not be able to provide the best solutions to problems faced by a collaborative species. References Bernstein, D. S., Givan, R., Immerman, N., & Zilberstein, S. (2002). The complexity of decentralized control of markov decision processes. Mathematics of Operations Research, 27(4), 819–840. Hutchins, E. (1995). Cognition in the Wild. MIT Press. Lungarella, M., & Sporns, O. (2005). Information self-structuring: Key principle for learning and development. Proceedings the fourth international conference on development and learning 2005, 25–30. Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information, Henry Holt and Co. Inc., New York, NY. Tomasello, M., Carpenter, M., Call, J., Behne, T., & Moll, H. (2005). Understanding and sharing intentions: the origins of cultural cognition. Behavioral and Brain Sciences, 28(5), 675–691; discussion 691–735.

Modeling collaborative coordination requires anthropological insights.

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