Title: Strategic play by resource-bounded agents in security games
Many studies have shown that humans are ``predictably irrational'': they do not act in a fully rational way, but their deviations from rational behavior are quite systematic. Our goal is to see the extent to which we can explain and justify these deviations as the outcome of rational but resource-bounded agents doing as well as they can, given their limitations. We focus on the well-studied ranger-poacher game, where rangers are trying to protect a number of sites from poaching. We capture the computational limitations by modeling the poacher and the ranger as probabilistic finite automata (PFAs). We show that, with sufficiently large memory, PFAs learn to play the Nash equilibrium (NE) strategies of the game and achieve the NE utility. However, if we restrict the memory, we get more ``human-like'' behaviors, such as probability matching (i.e., visiting sites in proportion to the probability of a rhino being there), and avoiding sites where there was a bad outcome (e.g., the poacher was caught by the ranger), that we also observed in experiments conducted on Amazon Mechanical Turk. Interestingly, we find that adding human-like behaviors such as probability matching and overweighting significant events (like getting caught) actually improves performance, showing that this seemingly irrational behavior can be quite rational. more »« less
Liu, X.; Halpern, J.Y.(
, Proceedings of the 36th Conference on Uncertainty in AI (UAI 2020))
null
(Ed.)
While traditional economics assumes that humans are fully rational
agents who always maximize their expected utility, in practice, we
constantly observe apparently irrational behavior. One explanation is
that people have limited computational power, so that they are, quite
rationally, making the best decisions they can, given their
computational limitations. To test this hypothesis, we consider the
multi-armed bandit (MAB) problem. We examine a simple strategy for
playing an MAB that can be implemented easily by a probabilistic finite automaton (PFA).
Roughly speaking, the PFA sets certain expectations, and plays an arm
as long as it meets them. If the PFA has sufficiently many states, it performs near-optimally. Its performance degrades gracefully as the number of states
decreases. Moreover, the PFA acts in a ``human-like'' way,
exhibiting a number of standard human biases, like an optimism
bias and a negativity bias.
Liu, Xinming; Halpern, Joseph Y.(
, Proceedings of the 36th Conference on Uncertainty in AI (UAI 2020))
While traditional economics assumes that humans are fully rational agents who always maximize their expected utility, in practice, we constantly observe apparently irrational behavior. One explanation is that people have limited computational power, so that they are, quite rationally, making the best decisions they can, given their computational limitations. To test this hypothesis, we consider the multi-armed bandit (MAB) problem. We examine a simple strategy for playing an MAB that can be implemented easily by a probabilistic finite automaton (PFA). Roughly speaking, the PFA sets certain expectations, and plays an arm as long as it meets them. If the PFA has sufficiently many states, it performs near-optimally. Its performance degrades gracefully as the number of states decreases. Moreover, the PFA acts in a “human-like” way, exhibiting a number of standard human biases, like an optimism bias and a negativity bias.
Oey, Lauren; Schachner, Adena; Vul, Edward(
, The Proceedings of the Annual Meeting of the Cognitive Science Society)
The human ability to deceive others and detect deception has long been tied to theory of mind. We make a stronger argument: in order to be adept liars – to balance gain (i.e. maximizing their own reward) and plausibility (i.e. maintaining a realistic lie) – humans calibrate their lies under the assumption that their partner is a rational, utility-maximizing agent. We develop an adversarial recursive Bayesian model that aims to formalize the behaviors of liars and lie detectors. We compare this model to (1) a model that does not perform theory of mind computations and (2) a model that has perfect knowledge of the opponent’s behavior. To test these models, we introduce a novel dyadic, stochastic game, allowing for quantitative measures of lies and lie detection. In a second experiment, we vary the ground truth probability. We find that our rational models qualitatively predict human lying and lie detecting behavior better than the non-rational model. Our findings suggest that humans control for the extremeness of their lies in a manner reflective of rational social inference. These findings provide a new paradigm and formal framework for nuanced quantitative analysis of the role of rationality and theory of mind in lying and lie detecting behavior.
Witt, Christopher C; Gadek, Chauncey R; Cartron, Jean-Luc E; Andersen, Michael J; Campbell, Mariel L; Castro-Farías, Marialejandra; Gyllenhaal, Ethan F; Johnson, Andrew B; Malaney, Jason L; Montoya, Kyana N; et al(
, Environmental Research)
Per- and polyfuoroalkyl substances (PFAS) in the environment pose persistent and complex threats to human
and wildlife health. Around the world, PFAS point sources such as military bases expose thousands of populations
of wildlife and game species, with potentially far-reaching implications for population and ecosystem health. But
few studies shed light on the extent to which PFAS permeate food webs, particularly ecologically and taxonomically diverse communities of primary and secondary consumers. Here we conducted >2000 assays to
measure tissue-concentrations of 17 PFAS in 23 species of mammals and migratory birds at Holloman Air Force
Base (AFB), New Mexico, USA, where wastewater catchment lakes form biodiverse oases. PFAS concentrations
were among the highest reported in animal tissues, and high levels have persisted for at least three decades.
Twenty of 23 species sampled at Holloman AFB were heavily contaminated, representing middle trophic levels
and wetland to desert microhabitats, implicating pathways for PFAS uptake: ingestion of surface water, sediments, and soil; foraging on aquatic invertebrates and plants; and preying upon birds or mammals. The hazardous long carbon-chain form, perfuorooctanosulfonic acid (PFOS), was most abundant, with liver
concentrations averaging >10,000 ng/g wet weight (ww) in birds and mammals, respectively, and reaching as
high 97,000 ng/g ww in a 1994 specimen. Perfuorohexanesulfonic acid (PFHxS) averaged thousands of ng/g ww
in the livers of aquatic birds and littoral-zone house mice, but one order of magnitude lower in the livers of
upland desert rodent species. Piscivores and upland desert songbirds were relatively uncontaminated. At control
sites, PFAS levels were strikingly lower on average and different in composition. In sum, legacy PFAS at this
desert oasis have permeated local aquatic and terrestrial food webs across decades, severely contaminating
populations of resident and migrant animals, and exposing people via game meat consumption and outdoor
recreation.
We propose a criterion of stability for two-sided markets with asymmetric information. A central idea is to formulate off-path beliefs conditional on counterfactual pairwise deviations and on-path beliefs in the absence of such deviations. A matching-belief configuration is stable if the matching is individually rational with respect to the system of on-path beliefs and is not blocked with respect to the system of off-path beliefs. The formulation provides a language for assessing matching outcomes with respect to their supporting beliefs and opens the door to further belief-based refinements. The main refinement analyzed in the paper requires the Bayesian consistency of on-path and off-path beliefs with prior beliefs. We define concepts of Bayesian efficiency, the rational expectations competitive equilibrium, and the core. Their contrast with pairwise stability manifests the role of information asymmetry in matching formation. (JEL C78, D40, D82, D83)
X. Liu, and J. Y. Halpern. Strategic play by resource-bounded agents in security games. Retrieved from https://par.nsf.gov/biblio/10414208. Proceedings of the 22nd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023) .
X. Liu, & J. Y. Halpern. Strategic play by resource-bounded agents in security games. Proceedings of the 22nd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023), (). Retrieved from https://par.nsf.gov/biblio/10414208.
X. Liu, and J. Y. Halpern.
"Strategic play by resource-bounded agents in security games". Proceedings of the 22nd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023) (). Country unknown/Code not available. https://par.nsf.gov/biblio/10414208.
@article{osti_10414208,
place = {Country unknown/Code not available},
title = {Strategic play by resource-bounded agents in security games},
url = {https://par.nsf.gov/biblio/10414208},
abstractNote = {Many studies have shown that humans are ``predictably irrational'': they do not act in a fully rational way, but their deviations from rational behavior are quite systematic. Our goal is to see the extent to which we can explain and justify these deviations as the outcome of rational but resource-bounded agents doing as well as they can, given their limitations. We focus on the well-studied ranger-poacher game, where rangers are trying to protect a number of sites from poaching. We capture the computational limitations by modeling the poacher and the ranger as probabilistic finite automata (PFAs). We show that, with sufficiently large memory, PFAs learn to play the Nash equilibrium (NE) strategies of the game and achieve the NE utility. However, if we restrict the memory, we get more ``human-like'' behaviors, such as probability matching (i.e., visiting sites in proportion to the probability of a rhino being there), and avoiding sites where there was a bad outcome (e.g., the poacher was caught by the ranger), that we also observed in experiments conducted on Amazon Mechanical Turk. Interestingly, we find that adding human-like behaviors such as probability matching and overweighting significant events (like getting caught) actually improves performance, showing that this seemingly irrational behavior can be quite rational.},
journal = {Proceedings of the 22nd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023)},
author = {X. Liu and J. Y. Halpern},
}
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