Results and problems in games of timing

Radzik, Tadeusz

doi:10.1214/lnms/1215453577

Cited by 14 publications

(10 citation statements)

References 27 publications

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“…While our empirical expected value model successfully predicts each player’s instantaneous prospects, it suffers from a key drawback: because it is conditioned on both players’ observed (and coupled) strategies, it does little to disentangle the effects of each player’s decisions on the trial’s outcome. However, our task bears a strong resemblance to the class of differential games known as duels 14,20,22 , in which players continuously evaluate options but choose only a single action. Along similar lines, we chose to analyze the expected value of the participant changing direction a final time and continuing on a straight-line trajectory thereafter.…”

Section: Resultsmentioning

confidence: 99%

“…Their continuous, dynamic interaction thus poses a challenge to any computational framework for the study of social decisions 4,17,18 . Moreover, while game theory has made progress in generalizing optimal strategies for games in continuous time and space, such as duels and auction bidding 4,14,19–22 , considerably less has been done to quantify highly dynamic behavior in cases where optimal strategies remain challenging to compute. While game theory has proven highly successful in analyzing various sorts of equilibria players might settle into, considerably less is known about the processes by which these equilibria are reached 23,24 .…”

Section: Introductionmentioning

confidence: 99%

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Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game

et al. 2019

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Previous studies of strategic social interaction in game theory have predominantly used games with clearly-defined turns and limited choices. Yet, most real-world social behaviors involve dynamic, coevolving decisions by interacting agents, which poses challenges for creating tractable models of behavior. Here, using a game in which humans competed against both real and artificial opponents, we show that it is possible to quantify the instantaneous dynamic coupling between agents. Adopting a reinforcement learning approach, we use Gaussian Processes to model the policy and value functions of participants as a function of both game state and opponent identity. We found that higher-scoring participants timed their final change in direction to moments when the opponent’s counter-strategy was weaker, while lower-scoring participants less precisely timed their final moves. This approach offers a natural set of metrics for facilitating analysis at multiple timescales and suggests new classes of experimental paradigms for assessing behavior.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game

et al. 2019

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“…To make strategic decisions about the mitigation of stealthy attacks, a central consideration must be the notion of time, which has been the focus of the games of timing literature stemming from the cold war period (see, for example, [4]). This research field received a new influx of work on the so-called FlipIt game beginning with research by Dijk et al [1] focused on the competitive dynamics to control a contested resource in a limited information environment.…”

Section: Introductionmentioning

confidence: 99%

Time-Dependent Strategies in Games of Timing

Merlevede

Johnson

Großklags

et al. 2019

Lecture Notes in Computer Science

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Timing, a central aspect of decision-making in security scenarios, is a subject of growing academic interest; frequently in the context of stealthy attacks, or advanced persistent threats (APTs). A key model in this research landscape is FlipIt [1]. However, a limiting simplifying assumption in the FlipIt literature is that costs and gains are not subject to discounting, which contradicts the typical treatment of decision-making over time in most economically relevant contexts. Our recent work [2] introduces an adaptation of the FlipIt model that applies time-based exponential discounting to the value of a protected resource, while allowing players to choose from among the same canonical strategies as in the original game. This paper extends the study of games of timing by introducing two new classes of strategies that are fundamentally motivated by a time-discounted world view. Within our game model, we compute player utilities, best responses and give a partial characterization of the game's Nash equilibria. Our model allows us to re-interpret the APT model using a finite total valuation, and a finite time horizon. By applying time-based discounting to the entire decision-making framework, we increase the level of realism as well as applicability to organizational security management.

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“…Scholars have studied such time-related aspects of tactical security choices since the cold war era by primarily focusing on zero-sum games called games of timing [5]. The theoretical contributions on some subclasses of these games have been surveyed by [41].…”

Section: A Security Economics and Games Of Timingmentioning

confidence: 99%

How Task Familiarity and Cognitive Predispositions Impact Behavior in a Security Game of Timing

Großklags

Reitter

2014

2014 IEEE 27th Computer Security Foundations Symposium

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This paper addresses security and safety choices that involve a decision on the timing of an action. Examples of such decisions include when to check log files for intruders and when to monitor financial accounts for fraud or errors. To better understand how performance in timing-related security situations is shaped by individuals' cognitive predispositions, we effectively combine survey measures with economic experiments. Two behavioral experiments are presented in which the timing of online security actions is the critical decision-making factor. The feedback modality in the decision-environment is varied between visual feedback with history (Experiment 1), and temporal feedback without history (Experiment 2). Using psychometric scales, we study the role of individual difference variables, specifically risk propensity and need for cognition. The analysis is based on the data from over 450 participants. We find that risk propensity is not a hindrance in timing tasks. Participants of average risk propensity generally benefit from a reflective disposition (high need for cognition), particularly when visual feedback is given. Overall, participants benefit from need for cognition; however, in the more difficult, temporal-estimation task, this requires familiarity with the task.

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Results and problems in games of timing

Cited by 14 publications

References 27 publications

Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game

Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game

Time-Dependent Strategies in Games of Timing

How Task Familiarity and Cognitive Predispositions Impact Behavior in a Security Game of Timing

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