Abstract
A general definition of the minimum required structure of computer agents that are capable of accurately representing human behavior in agent-based models is offered. Included is the abstract definition of the Environment, the State Vector, the Perceptor, the Actor, and the Ratiocinator components of the agent structure and their interaction. The synthetic population and associated incident distributions are then defined. Finally, practical considerations of time, accuracy and path dependency are examined.
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Notes
- 1.
A wonderful experiment would be to use a virtual market representation of a stock market to test the hypothesis that the confidence interval on any market forecast is so wide as to make moot the forecast itself. In other words, the forecast can never be better than a random guess.
- 2.
Agent-based models are finding some interesting uses in this context, where one or more of the agents in the simulation are actually people. It’s obvious that this is the case in many electronic simulation games, where the player manages an avatar which behaves under the direction of the player and has no independent volition. .
References
Parker, R.A., Bakken, D.: Predicting the unpredictable: agent-based models in market research. In: Mouncey, P., Wimmer, F. (eds.) Best Practices in Market Research. Wiley, Hoboken (2007)
Parker, R.A., Perroud, D.: Exploring markets with agent-based computer modeling. In: Proceedings of the 61st ESOMAR International Congress, Montreal, Canada (2008)
Parker, R.A.: An agent-based simulation of air travel itinerary choice. In: Proceedings of the ABMTRANS 2017 Conference, Madeira, Portugal (2017). https://doi.org/10.1016/j.procs.2017.05.419
Parker, R.A.: The concept of narrative as a fundamental for human agent-based modeling. In: Karwowski, W., Abram, T. (eds.) Intelligent Human Systems Integration. Advances in Intelligent Systems and Computing, vol. 722 (2018). https://doi.org/10.1007/978-3-319-73888-8_59
Conway, J.: Mathematical games: the game of life. In: Gardner, M. (ed.) Scientific American (1970)
Epstein, J., Axtell, R.: Growing Artificial Societies: Social Sciences from the Bottom Up. MIT Press, Cambridge (1996)
Parker, R.A.: A demonstration of AirVM. In: Proceedings of the AGIFORS Schedule and Strategic Planning Study Group, Lausanne, Switzerland (2010)
Rossi, P., Allenby, G., McCulloch, R.: Bayesian Statistics and Marketing. Wiley, Hoboken (2005)
Bass, F.: A new-product growth model for consumer durables. Manage. Sci. 15, 215–227 (1969)
Humphreys, P.: Extending Ourselves: Computational Science, Empiricism, and Scientific Method. Oxford University Press, Oxford (2004)
Parker, R.A., Holmes, B.: Using predictive analytics for corporate shuttle decisions. In: Proceedings of the NBAA Convention, Las Vegas, Nevada, USA (2015)
Grimm, V., Railsback, S.: Individual-Based Modeling and Ecology. Princeton University Press, Princeton (2005)
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Parker, R.A. (2019). The Construction of Agent Simulations of Human Behavior. In: Karwowski, W., Ahram, T. (eds) Intelligent Human Systems Integration 2019. IHSI 2019. Advances in Intelligent Systems and Computing, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-030-11051-2_66
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DOI: https://doi.org/10.1007/978-3-030-11051-2_66
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