Rage against the machines: how subjects play against learning algorithms

Duersch, Peter; Kolb, Albert; Oechssler, Jörg; Schipper, Burkhard C.

doi:10.1007/s00199-009-0446-0

Cited by 37 publications

(21 citation statements)

References 24 publications

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“…However, this also seems to be driven by incentives as variances in lab-np and lab-f are not significantly different from those in 7 See our companion paper for details about the learning processes (Duersch et al 2009). 8 In a different context, Shavit et al (2001) find that bids in a lottery evaluation task are significantly higher on the internet than in a classroom experiment.…”

Section: Confirming Findings By Anderhub Et Al (2001) and Shavit Et mentioning

confidence: 95%

Incentives for subjects in internet experiments

Duersch¹,

Oechssler²,

Schipper³

2009

Economics Letters

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Internet experiments are a new and convenient way for reaching a large subject pool. Yet, providing incentives to subjects can be a tricky design issue. One cost-effective and simple method is the publication of a high score (as in computer games). We test whether a high score provides adequate and non-distortionary incentives by comparing it to the usual performance based incentives. We find significant differences and conclude that high scores are not always appropriate as an incentive device. Performance based financial incentives seem to be required also in internet experiments.JEL codes: C72; C91; C92; D43; L13.

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Section: Confirming Findings By Anderhub Et Al (2001) and Shavit Et mentioning

confidence: 95%

Incentives for subjects in internet experiments

Duersch¹,

Oechssler²,

Schipper³

2009

Economics Letters

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“…To gain some more confidence that our Learning Condition really accurately picks up learning dynamics - 14 The typical argument claims that, since a rational selfish player chooses the lowest possible contribution (i.e. zero), any confusion would lead to a positive and therefore higher contribution.…”

Section: Learning Versus Conditional Cooperationmentioning

confidence: 99%

Confusion and learning in the voluntary contributions game

2013

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The focus for the Centre is research into individual and strategic decision-making using a combination of theoretical and experimental methods. On the theory side, members of the Centre investigate individual choice under uncertainty, cooperative and non-cooperative game theory, as well as theories of psychology, bounded rationality and evolutionary game theory. Members of the Centre have applied experimental methods in the fields of public economics, individual choice under risk and uncertainty, strategic interaction, and the performance of auctions, markets and other economic institutions. Much of the Centre's research involves collaborative projects with researchers from other departments in the UK and overseas. AbstractWe use a limited information environment to assess the role of confusion in the repeated voluntary contributions game. A comparison with play in a standard version of the game suggests, that the common claim that decision errors due to confused subjects biases estimates of cooperation upwards, is not necessarily correct. Furthermore, we find that simple learning cannot generate the kind of contribution dynamics commonly attributed to the existence of conditional cooperators. We conclude that cooperative behavior and its decay observed in public goods games is not a pure artefact of confusion and learning.

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“…Hence, subjects' choice of the dominated action Soft and in alternation with Tough, is a strong indication that they are incurring costs to teach the other to take turns. 2 1 Duersch et al (2010) study how subjects learn to play against computers that are programmed to follow one of a number of standard learning algorithms. They find that teaching occurs frequently and that all learning algorithms are subject to exploitation with the notable exception of imitation.…”

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confidence: 99%

Learning, teaching, and turn taking in the repeated assignment game

2012

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History-dependent strategies are often used to support cooperation in repeated game models. Using the indefinitely repeated common-pool resource assignment game and a perfect stranger experimental design, this paper reports novel evidence that players who have successfully used an efficiency-enhancing turn-taking strategy will teach other players in subsequent supergames to adopt this strategy. We find that subjects engage in turn taking frequently in both the Low Conflict and the High Conflict treatments. Prior experience with turn taking significantly increases turn taking in both treatments. Moreover, successful turn taking often involves fast learning, and individuals with turn taking experience are more likely to be teachers than inexperienced individuals. The comparative statics results show that teaching in such an environment also responds to incentives, since teaching is empirically more frequent in the Low Conflict treatment with higher benefits and lower costs.JEL Classification: C73, C91

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Rage against the machines: how subjects play against learning algorithms

Cited by 37 publications

References 24 publications

Incentives for subjects in internet experiments

Incentives for subjects in internet experiments

Confusion and learning in the voluntary contributions game

Learning, teaching, and turn taking in the repeated assignment game

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