Strategic Style in Pared-Down Poker

  • Kevin Burns


This chapter deals with the manner of making diagnoses and decisions, called strategic style, in a gambling game called Pared-down Poker. The approach treats style as a mental mode in which choices are constrained by expected utilities. The focus is on two classes of utility, i.e., money and effort, and how cognitive styles compare to normative strategies in optimizing these utilities. The insights are applied to real-world concerns like managing the war against terror networks and assessing the risks of system failures. After “Introducing the Interactions” involved in playing poker, the contents are arranged in four sections, as follows. “Underpinnings of Utility” outlines four classes of utility and highlights the differences between them: economic utility (money), ergonomic utility (effort), informatic utility (knowledge), and aesthetic utility (pleasure). “Inference and Investment” dissects the cognitive challenges of playing poker and relates them to real-world situations of business and war, where the key tasks are inference (of cards in poker, or strength in war) and investment (of chips in poker, or force in war) to maximize expected utility. “Strategies and Styles” presents normative (optimal) approaches to inference and investment, and compares them to cognitive heuristics by which people play poker–-focusing on Bayesian methods and how they differ from human styles. The normative strategy is then pitted against cognitive styles in head-to-head tournaments, and tournaments are also held between different styles. The results show that style is ergonomically efficient and economically effective, i.e., style is smart. “Applying the Analysis” explores how style spaces, of the sort used to model individual behavior in Pared-down Poker, might also be applied to real-world problems where organizations evolve in terror networks and accidents arise from system failures.


Expected Utility Game Tree Economic Utility Poker Player Hand Strength 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.The MITRE CorporationBedfordUSA

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