Foundations Of Statistics

หนงสอ Behavioral Game Theory: Experiments in Strategic Interaction เขยนโดย Colin F. Camerer มวตถประสงคเพอนำเสนอหลกฐานเชงประจกษจากผลการวจยจำนวนมากมายทยนยนอทธพลของปจจยทางจตวทยาทมผลตอการตดสนใจตามทฤษฎเกม หนงสอ เลมนไดนำเสนอแนวคดทฤษฎทเพมความสามารถในการอธบายพฤตกรรมการตดสนใจตามทฤษฎเกม (Game theory) ซง von Neumann; & Morgenstern ไดเสนอไวในป ค.ศ. 1944 โดยชใหเหนวา ปจจยทางจตวทยามอทธพลทำใหการตดสนใจทเกดขนจรงคลาดเคลอนจากการคาดการณของทฤษฎเกม

Behavioral Game Theory: Experiments in Strategic Interaction

Bilevel optimization is defined as a mathematical program, where an optimization problem contains another optimization problem as a constraint. These problems have received significant attention from the mathematical programming community. Only limited work exists on bilevel problems using evolutionary computation techniques; however, recently there has been an increasing interest due to the proliferation of practical applications and the potential of evolutionary algorithms in tackling these problems. This paper provides a comprehensive review on bilevel optimization from the basic principles to solution strategies; both classical and evolutionary. A number of potential application problems are also discussed. To offer the readers insights on the prominent developments in the field of bilevel optimization, we have performed an automated text-analysis of an extended list of papers published on bilevel optimization to date. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area.

A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Tackling Climate Change with Machine Learning

A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications.

While three deployed applications of game theory for security have recently been reported at AAMAS [12], we as a community remain in the early stages of these deployments; there is a continuing need to understand the core principles for innovative security applications of game theory. Towards that end, this paper presents PROTECT, a game-theoretic system deployed by the United States Coast Guard (USCG) in the port of Boston for scheduling their patrols. USCG has termed the deployment of PROTECT in Boston a success, and efforts are underway to test it in the port of New York, with the potential for nationwide deployment.PROTECT is premised on an attacker-defender Stackelberg game model and offers five key innovations. First, this system is a departure from the assumption of perfect adversary rationality noted in previous work, relying instead on a quantal response (QR) model of the adversary's behavior --- to the best of our knowledge, this is the first real-world deployment of the QR model. Second, to improve PROTECT's efficiency, we generate a compact representation of the defender's strategy space, exploiting equivalence and dominance. Third, we show how to practically model a real maritime patrolling problem as a Stackelberg game. Fourth, our experimental results illustrate that PROTECT's QR model more robustly handles real-world uncertainties than a perfect rationality model. Finally, in evaluating PROTECT, this paper for the first time provides real-world data: (i) comparison of human-generated vs PROTECT security schedules, and (ii) results from an Adversarial Perspective Team's (human mock attackers) analysis.

/pdf/protect-a-deployed-game-theoretic-system-to-protect-the-45xyusfw2j.pdf

PROTECT: a deployed game theoretic system to protect the ports of the United States

Recent deployments of Stackelberg security games (SSG) have led to two competing approaches to handle boundedly rational human adversaries: (1) integrating models of human (adversary) decision-making into the game-theoretic algorithms, and (2) applying robust optimization techniques that avoid adversary modeling. A recent algorithm (MATCH) based on the second approach was shown to outperform the leading modeling-based algorithm even in the presence of significant amount of data. Is there then any value in using human behavior models in solving SSGs? Through extensive experiments with 547 human subjects playing 11102 games in total, we emphatically answer the question in the affirmative, while providing the following key contributions: (i) we show that our algorithm, SU-BRQR, based on a novel integration of human behavior model with the subjective utility function, significantly outperforms both MATCH and its improvements; (ii) we are the first to present experimental results with security intelligence experts, and find that even though the experts are more rational than the Amazon Turk workers, SU-BRQR still outperforms an approach assuming perfect rationality (and to a more limited extent MATCH); (iii) we show the advantage of SU-BRQR in a new, large game setting and demonstrate that sufficient data enables it to improve its performance over MATCH.

/pdf/analyzing-the-effectiveness-of-adversary-modeling-in-2or6yxi93d.pdf

Analyzing the effectiveness of adversary modeling in security games

Illegal poaching is an international problem that leads to the extinction of species and the destruction of ecosystems. As evidenced by dangerously dwindling populations of endangered species, existing anti-poaching mechanisms are insufficient. This paper introduces the Protection Assistant for Wildlife Security (PAWS) application - a joint deployment effort done with researchers at Uganda's Queen Elizabeth National Park (QENP) with the goal of improving wildlife ranger patrols. While previous works have deployed applications with a game-theoretic approach (specifically Stackelberg Games) for counter-terrorism, wildlife crime is an important domain that promotes a wide range of new deployments. Additionally, this domain presents new research challenges and opportunities related to learning behavioral models from collected poaching data. In addressing these challenges, our first contribution is a behavioral model extension that captures the heterogeneity of poachers' decision making processes. Second, we provide a novel framework, PAWS-Learn, that incrementally improves the behavioral model of the poacher population with more data. Third, we develop a new algorithm, PAWS-Adapt, that adaptively improves the resource allocation strategy against the learned model of poachers. Fourth, we demonstrate PAWS's potential effectiveness when applied to patrols in QENP, where PAWS will be deployed.

http://teamcore.usc.edu/papers/2014/aamas2014_yang.pdf

Adaptive resource allocation for wildlife protection against illegal poachers

Recent real-world deployments of Stackelberg security games make it critical that we address human adversaries' bounded rationality in computing optimal strategies. To that end, this paper provides three key contributions: (i) new efficient algorithms for computing optimal strategic solutions using Prospect Theory and Quantal Response Equilibrium; (ii) the most comprehensive experiment to date studying the effectiveness of different models against human subjects for security games; and (iii) new techniques for generating representative payoff structures for behavioral experiments in generic classes of games. Our results with human subjects show that our new techniques outperform the leading contender for modeling human behavior in security games.

Improving resource allocation strategy against human adversaries in security games

http://www.cs.utep.edu/kiekintveld/papers/2013/ykotj-human-adversaries.pdf

Rong Yang

Papers

PROTECT: a deployed game theoretic system to protect the ports of the United States

Analyzing the effectiveness of adversary modeling in security games

Adaptive resource allocation for wildlife protection against illegal poachers

Improving resource allocation strategy against human adversaries in security games

Improving resource allocation strategies against human adversaries in security games: An extended study