No abstract

Intrusion Detection Systems (IDSs) help in creating cyber situational awareness for defenders by providing recommendations. Prior research in simulation and game-theory has revealed that the presence and accuracy of IDS-like recommendations influence the decisions of defenders and adversaries. In the current paper, we present novel analyses of prior research by analyzing the sequential decisions of defenders and adversaries over repeated trials. Specifically, we developed computational cognitive models based upon Instance-Based Learning Theory (IBLT) to capture the dynamics of the sequential decisions made by defenders and adversaries across numerous conditions that differed in the IDS's availability and accuracy. We found that cognitive mechanisms based upon recency, frequency, and variability helped account for adversarial and defender decisions better than the optimal Nash solutions. We discuss the implications of our results for adversarial-and-defender decisions in the cyber-world.

The Prisoner's Dilemma (PD) is a classic decision problem where 2 players simultaneously must decide whether to cooperate or to act in their own narrow self-interest. The PD game has been used to model many naturally occurring interactive situations, at the personal, organizational, and social levels, in which there exists a tension between individual material gain and the common good. At least 2 factors may influence the emergence of cooperative behavior in this well-known collective action problem: the incentive structure of the game itself, and the intrinsic social preferences of each of the players. We present a framework that integrates these 2 factors in an effort to account for patterns of high or low cooperation from repeated choice interactions. In an experiment using a collection of different PD games, and a measure of individual social preferences, we identify regions of PD games in which (a) cooperation is independent of social preferences; (b) nice people can be exploited; and (c) being nice is consistently rewarded.

Global supply networks in agriculture, manufacturing, and services are a defining feature of the modern world. The efficiency and the distribution of surpluses across different parts of these networks depend on the choices of intermediaries. This paper conducts price formation experiments with human subjects located in large complex networks to develop a better understanding of the principles governing behavior. Our first experimental finding is that prices are larger and that trade is significantly less efficient in small-world networks as compared to random networks. Our second experimental finding is that location within a network is not an important determinant of pricing. An examination of the price dynamics suggests that traders on cheapest-and hence active-paths raise prices while those off these paths lower them. We construct an agent-based model (ABM) that embodies this rule of thumb. Simulations of this ABM yield macroscopic patterns consistent with the experimental findings. Finally, we extrapolate the ABM on to significantly larger random and smallworld networks and find that network topology remains a key determinant of pricing and efficiency. Globalization is a prominent feature of the modern economy 1. Nowadays, supply, service and trading chains 2,3,3-6 play a central role in different contexts such as agriculture 7-10 , transport and communication networks 11,12 , international trade 13 and finance 14,15. One key question on these systems is how pricing dynamics by intermediaries of the economy impacts both efficiency and surpluses. The purpose of this paper is to develop a better understanding of the forces that shape intermediary pricing behavior in such complex networks. Game theory constitutes a useful framework to study competition among trading agents 16. In this context, the Nash Bargaining Game 17 studies how two agents share a surplus that they can jointly generate. In the Nash Bargaining Game, two players demand a portion of some good. If the total amount requested by both players is less than the total value of the good, both players get their request; otherwise, no player gets their request. There are many Nash equilibria in this game: any combination of demands whose sum is equal to the total value of the good constitutes a Nash equilibrium. There is also a Nash equilibrium where each player demands the entire value of the good 18. As a generalization of Nash demand game to n players, Choi et al. 19 proposed and tested in the laboratory a model of intermediation pricing. In this model, a good is supposed to go from a source S to a destination D. Intermediaries, which are located in the nodes of a network, may post a price for the passage of the good. Trading occurs if there exists a path between S and D on which the sum of prices is smaller than or equal to the value of the good. The key finding was that the pricing and the surpluses of the intermediaries depends on the presence of "critical" nodes: a node is said to be critical if it lies on all possible paths between S and D. Condo...

Game Theory is a common approach used to understand attacker and defender motives, strategies, and allocation of limited security resources. For example, many defense algorithms are based on game-theoretic solutions that conclude that randomization of defense actions assures unpredictability, creating difficulties for a human attacker. However, many game-theoretic solutions often rely on idealized assumptions of decision making that underplay the role of human cognition and information uncertainty. The consequence is that we know little about how effective these algorithms are against human players. Using a simplified security game, we study the type of attack strategy and the uncertainty about an attacker's strategy in a laboratory experiment where participants play the role of defenders against a simulated attacker. Our goal is to compare a human defender's behavior in three levels of uncertainty (Information Level: Certain, Risky, Uncertain) and three types of attacker's strategy (Attacker's strategy: Minimax, Random, Adaptive) in a between-subjects experimental design. Best defense performance is achieved when defenders play against a minimax and a random attack strategy compared to an adaptive strategy. Furthermore, when payoffs are certain, defenders are as efficient against random attack strategy as they are against an adaptive strategy, but when payoffs are uncertain, defenders have most difficulties defending against an adaptive attacker compared to a random attacker. We conclude that given conditions of uncertainty in many security problems, defense algorithms would be more efficient if they are adaptive to the attacker actions, taking advantage of the attacker's human inefficiencies.