A Cognitive Model of Dynamic Cooperation With Varied Interdependency Information

Abstract

We analyze the dynamics of repeated interaction of two players in the Prisoner's Dilemma (PD) under various levels of interdependency information and propose an instance-based learning cognitive model (IBL-PD) to explain how cooperation emerges over time. Six hypotheses are tested regarding how a player accounts for an opponent's outcomes: the selfish hypothesis suggests ignoring information about the opponent and utilizing only the player's own outcomes; the extreme fairness hypothesis weighs the player's own and the opponent's outcomes equally; the moderate fairness hypothesis weighs the opponent's outcomes less than the player's own outcomes to various extents; the linear increasing hypothesis increasingly weighs the opponent's outcomes at a constant rate with repeated interactions; the hyperbolic discounting hypothesis increasingly and nonlinearly weighs the opponent's outcomes over time; and the dynamic expectations hypothesis dynamically adjusts the weight a player gives to the opponent's outcomes, according to the gap between the expected and the actual outcomes in each interaction. When players lack explicit feedback about their opponent's choices and outcomes, results are consistent with the selfish hypothesis; however, when this information is made explicit, the best predictions result from the dynamic expectations hypothesis.

Using Cognitive Agents to Train Negotiation Skills

“…Prior work shows that cognitive agents can make realistic opponents and training partners in multi-person tasks. These agents provide a good account of behavior in a variety of multiplayer strategic games including the Prisoner's Dilemma (Gonzalez et al, 2014 ), Rock-Paper-Scissors (West et al, 2005 ), and even Backgammon (Sanner et al, 2000 ). Moreover, in complex cooperative tasks such as UAV piloting, teams with a cognitive agent can perform just as well as all-human teams (Ball et al, 2009 ).…”

Security under Uncertainty: Adaptive Attackers Are More Challenging to Human Defenders than Random Attackers

“…Yet, attackers foreseeing the way home owners behave, may be able to find simple and unexpected ways to break into the house (i.e., thorough the front door). Defending against intelligent unauthorized intrusions in the cyber world can be even more challenging, given the hyper-dimensionality of the environment, the type of digital weapons used, the speed of operations and large number of nodes to protect against a relative high number of potential attackers (Gonzalez et al, 2015). In this research we address a basic question of how do human defenders behave under several levels of uncertainty and various types of attack strategies.…”

“…To build effective dynamic and adaptive defense algorithms we need to address at least two strong assumptions in the science of security games (Nguyen et al, 2016) and behavioral game theory more generally (Gonzalez et al, 2015): information certainty and human rationality. Current defense algorithms inspired by game theory assume that a defender has perfect information about the payoff matrix and the attacker's behaviors.…”

“…In a review of learning models from behavioral game theory, Camerer (2003) concludes that most models rely on a full information assumption, and they would not be able to predict behavior in conditions where this information is not available. Similarly, most of these models make cognitively implausible assumptions, such as players being able to remember large amounts of information (Gonzalez et al, 2015). …”

“…This body of research suggests fundamental psychological differences in how humans make decisions under uncertainty (Hau et al, 2008; Hertwig and Erev, 2009). In behavioral game theory, uncertainty effects are also of great interest (Martin et al, 2014; Gonzalez et al, 2015). For example, Bereby-Meyer and Roth (2006) have studied players' abilities to learn cooperation in a repeated PD game where the payoff descriptions were more or less noisy: in each outcome, the payoffs were presented either deterministically or probabilistically.…”