On Learning To Become a Successful Loser: A Comparison of Alternative Abstractions of Learning Processes in the Loss Domain

Bereby‐Meyer, Yoella; Erev, Ido

doi:10.1006/jmps.1998.1214

Cited by 116 publications

(58 citation statements)

References 23 publications

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“…For instance, Bereby-Meyer and Erev (1998) found quicker learning in a probability learning experiment when payoffs were 2/ 2 rather than 4/0 or 0/ 4. The natural reference point of zero EV should make it easier to spot that a payoff has moved, particularly if-as was the case in our experiments-participants see a running points total that makes it easy to see whether points are decreasing or increasing.…”

Section: Direction-of-change Effectsmentioning

confidence: 99%

Doomed to repeat the successes of the past: History is best forgotten for repeated choices with nonstationary payoffs

Rakow

Miler

2009

Memory & Cognition

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Section: Direction-of-change Effectsmentioning

confidence: 99%

Doomed to repeat the successes of the past: History is best forgotten for repeated choices with nonstationary payoffs

Rakow

Miler

2009

Memory & Cognition

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“…5 In such environments, the argument goes, non-learning strategies get poor payoffs because the actors cannot respond to changing payoff structures by changing action. Actors that play an ESS in one situation, but cannot deal with changes to the environment, now do poorly against learners that reach this same ESS in the original situation and can re-adapt when necessary.…”

Section: The Evolution Of Learningmentioning

confidence: 99%

“…I also explored a learning rule outlined by Barrett [3], which I call Barrett Learning. This rule is in some ways similar to Adjustable Reference Point with Truncation learning introduced by Bereby and Erev [5]. Actors using this rule discount past experience compared to more recent experience.…”

Section: Short Term Success and Simulationmentioning

confidence: 99%

“…One can think of the ac- 27 There is experimental evidence supporting the use of rules where actors punish or forget strategies, i.e., sometimes decrement their reinforcements. See Bereby and Erev [5], for example. 28 The replicator equation determines how proportions of strategies in a population change under the replicator dynamics.…”

Section: Evolving To Generalizementioning

confidence: 99%

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Evolving to Generalize: Trading Precision for Speed

O’Connor

2017

The British Journal for the Philosophy of Science

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Biologists and philosophers of biology have argued that learning rules that do not lead organisms to play evolutionarily stable strategies (ESSes) in games will not be stable and thus not evolutionarily successful [21,12]. This claim, however, stands at odds with the fact that learning generalization-a behavior that cannot lead to ESSes when modeled in games-is observed throughout the animal kingdom [22]. In this paper, I use learning generalization to illustrate how previous analyses of the evolution of learning have gone wrong. It has been widely argued that the function of learning generalization is to allow for swift learning about novel stimuli. I show that in evolutionary game theoretic models learning generalization, despite leading to suboptimal behavior, can indeed speed learning. I further observe that previous analyses of the evolution of learning ignored the short term success of learning rules. If one drops this assumption, I argue, it can be shown that learning generalization will be expected to evolve in these models. I also use this analysis to show how ESS methodology can be misleading, and to reject previous justifications about ESS play derived from analyses of learning.

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“…More sophisticated learning dynamics also allow for punishment and forgetting. They typically do much better than simple Herrnstein reinforcement in games like those discussed in this paper [Barrett and Zollman (2009)], and, saliently, they often much better model the actual behavior of learners [Roth and Erev (1995)] [Bereby-Meyer, Yoella and Erev (1998)]. The methodological thought is that Herrnstein reinforcement learning requires only relatively weak dispositional resources and if it allows for successful coordinated action in a particular context, then one can expect a broad class of more sophisticated reinforcement dynamics to allow for similar success.…”

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confidence: 99%

Description and the Problem of Priors

Barrett

2014

Erkenn

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Abstract. Belief-revision models of knowledge describe how to update one's degrees of belief associated with hypotheses as one considers new evidence, but they typically do not say how probabilities become associated with meaningful hypotheses in the first place. Here we consider a variety of Skyrms-Lewis signaling game [Lewis (1969)] [Skyrms (2010)] where simple descriptive language and predictive practice and associated basic expectations coevolve. Rather than assigning prior probabilities to hypotheses in a fixed language then conditioning on new evidence, the agents begin with no meaningful language or expectations then evolve to have expectations conditional on their descriptions as they evolve to have meaningful descriptions for the purpose of successful prediction. The model, then, provides a simple but concrete example of how the process of evolving a descriptive language suitable for inquiry might also provide agents with conditional expectations that reflect the type and degree of predictive success in fact afforded by their evolved predictive practice. This illustrates one way in which the traditional problem priors may simply fail to apply to one's model of evolving inquiry. Description, Prediction, and ExpectationBelief-revision models of knowledge describe how to update one's degrees of belief as one considers new evidence. On a Bayesian model, for example, one fixes a descriptive language, sets coherent prior probabilities over a set of hypotheses expressed in the language, then updates one's degrees of belief as one conditions on new evidence. While such an account of reflective inquiry has many virtues, it has nothing to say concerning how to assign prior probabilities to meaningful hypotheses. This is the problem of priors.On reflection, one might, however, find the problem of priors itself puzzling.While it is indeed unclear what procedure one should adopt in assigning prior expectations to hypotheses expressed in a fixed descriptive language, it is similarly unclear how one might ever come to use such a language without already having a rich set of expectations. The symmetry of these reflections suggests a strategy.We will consider how it might be possible for basic expectations to coevolve with a simple descriptive language. More specifically, we will consider how simple for coordinated prediction might also also provide a core set of well-tuned posterior expectations that might then be available to constrain subjective degrees of belief should the agent turn to reflective inquiry.We will consider the story in three parts. Part I: The Coevolution of Description and PredictionA sender-predictor game is a variety of Skyrms-Lewis signaling game.2 In a sender-predictor game, however, the agents coevolve both descriptive and predictive dispositions. We will refer to these are the agents' first-order dispositions. Their second-order dispositions determine how they update their first-order dispositions as they learn from experience.

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On Learning To Become a Successful Loser: A Comparison of Alternative Abstractions of Learning Processes in the Loss Domain

Cited by 116 publications

References 23 publications

Doomed to repeat the successes of the past: History is best forgotten for repeated choices with nonstationary payoffs

Doomed to repeat the successes of the past: History is best forgotten for repeated choices with nonstationary payoffs

Evolving to Generalize: Trading Precision for Speed

Description and the Problem of Priors

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