A Behavioral Learning Process in Games

Laslier, Jean‐François; Topol, Richard; Walliser, Bernard

doi:10.1006/game.2000.0841

Cited by 69 publications

(49 citation statements)

References 22 publications

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“…This paper studies the reinforcement learning process introduced by Roth and Erev (1995), and Erev and Roth (1998), also referred to as cumulative proportional reinforcement learning in Laslier et al (2001).…”

Section: The Modelmentioning

confidence: 99%

“…This paper studies the analytical properties of the reinforcement learning model proposed in Erev and Roth (1998), also termed cumulative reinforcement learning in Laslier et al (2001). The stochastic model of learning accounts for two main elements: the Law of E¤ect (positive reinforcement of actions that perform well) and the Power Law of Practice (learning curves tend to be steeper initially).…”

Section: Introductionmentioning

confidence: 99%

“…the Cross model as studied in Bn"orgers and Sarin (1997)), and most results available to date can only guarantee that in some reinforcement learning models, it may (e.g. the Erev and Roth model analyzed in Hopkins (2002), Beggs (2005) and Laslier et al (2001)). This paper studies the stochastic reinforcement learning model introduced by Roth and Erev (1995) and Erev and Roth (1998), also termed cumulative proportional reinforcement in Laslier et al (2001).…”

Section: Introductionmentioning

confidence: 99%

“…the Erev and Roth model analyzed in Hopkins (2002), Beggs (2005) and Laslier et al (2001)). This paper studies the stochastic reinforcement learning model introduced by Roth and Erev (1995) and Erev and Roth (1998), also termed cumulative proportional reinforcement in Laslier et al (2001). In the model, there is a …nite number of players who are to repeatedly play a normal form game with strictly positive payo¤s.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Learning strict Nash equilibria through reinforcement

Ianni

2014

Journal of Mathematical Economics

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This paper studies the analytical properties of the reinforcement learning model proposed in Erev and Roth (1998), also termed cumulative reinforcement learning in Laslier et al. (2001). The stochastic model of learning accounts for two main elements: the Law of E¤ect (positive reinforcement of actions that perform well) and the Power Law of Practice (learning curves tend to be steeper initially).The paper establishes a relation between the learning process and the underlying deterministic replicator equation. The main results show that if the solution trajectories of the latter converge su¢ ciently fast, then the probability that all the realizations of the learning process over a given spell of time, possibly in…nite, becomes arbitrarily close to one, from some time on. In particular, the paper shows that the property of fast convergence is always satis…ed in proximity of a strict Nash equilibrium.The results also provide an explicit estimate of the approximation error that could prove to be useful in empirical analysis.JEL: C72, C92, D83.

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Section: The Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning strict Nash equilibria through reinforcement

Ianni

2014

Journal of Mathematical Economics

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“…In net, subjects were solely motivated by their ranking on the highscore table. 19 Note that this difference is not about relative (net) versus absolute (lab) payoff maximization. A subjects needs to maximize his absolute payoff in order to achieve a large highscore.…”

Section: Experimenting On the Internet -Does It Make A Difference?mentioning

confidence: 99%

Rage Against the Machines: How Subjects Learn to Play Against Computers

et al. 2006

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We use an experiment to explore how subjects learn to play against computers which are programmed to follow one of a number of standard learning algorithms. The learning theories are (unbeknown to subjects) a best response process, fictitious play, imitation, reinforcement learning, and a trial & error process. We test whether subjects try to influence those algorithms to their advantage in a forward-looking way (strategic teaching). We find that strategic teaching occurs frequently and that all learning algorithms are subject to exploitation with the notable exception of imitation. The experiment was conducted, both, on the internet and in the usual laboratory setting. We find some systematic differences, which however can be traced to the different incentives structures rather than the experimental environment.JEL codes: C72; C91; C92; D43; L13.

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Applying the dynamics of evolution to achieve reliability in master–worker computing

Christoforou

Anta

Georgiou

et al. 2013

Concurrency and Computation

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SUMMARYWe consider Internet-based master-worker task computations, such as SETI@home, where a master process sends tasks, across the Internet, to worker processes; workers execute and report back some result. However, these workers are not trustworthy, and it might be at their best interest to report incorrect results. In such master-worker computations, the behavior and the best interest of the workers might change over time. We model such computations using evolutionary dynamics, and we study the conditions under which the master can reliably obtain task results. In particular, we develop and analyze an algorithmic mechanism based on reinforcement learning to provide workers with the necessary incentives to eventually become truthful. Our analysis identifies the conditions under which truthful behavior can be ensured and bounds the expected convergence time to that behavior. The analysis is complemented with illustrative simulations.

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A Behavioral Learning Process in Games

Cited by 69 publications

References 22 publications

Learning strict Nash equilibria through reinforcement

Learning strict Nash equilibria through reinforcement

Rage Against the Machines: How Subjects Learn to Play Against Computers

Applying the dynamics of evolution to achieve reliability in master–worker computing

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