Case-based learning with different similarity functions

Guerdjikova, Ani

doi:10.1016/j.geb.2007.10.004

Cited by 16 publications

(10 citation statements)

References 20 publications

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“…Note that Chmura et al [9] also find that self-tuning EWA and a selection of other simple learning models out-perform random choice, but they found self-tuning EWA was the best performing learning model in predicting individual choice with these data. 14 We use a non-nested model selection test proposed by Vuong [40], which provides a directional test of which model is favored in the data generating process. Testing the CBL model versus the RL model, the Vuong test statistic is 7.45, which is highly significant and favors the selection of the CBL model.…”

Section: Resultsmentioning

confidence: 99%

“…The second class is dynamic, but it utilizes simulations to show that case-based models match population dynamics rather than econometric techniques to find parameters [4,5]. The third class is experimental investigations of different aspects of case-based decision-making [13][14][15][16][17][18]. Our study is unique in that it proposes a stochastic choice framework to estimate a dynamic case-based decision process on game theoretic observations from the lab.…”

Section: Introductionmentioning

confidence: 99%

“…The mean squared error is 0.1618 for RL, 0.1715 for self-tuning EWA, and 0.1603 for CBL, where the ordering of selection of models is the same as the quadratic scoring rule 14. We estimate the initial attractions in our self-tuning EWA model while Chmura et al[9] do not, which does not appear to make much of a difference in goodness of fit.…”

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confidence: 99%

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Estimating Case-Based Learning

Guilfoos

Pape

2020

Games

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We propose a framework in order to econometrically estimate case-based learning and apply it to empirical data from twelve 2 × 2 mixed strategy equilibria experiments. Case-based learning allows agents to explicitly incorporate information available to the experimental subjects in a simple, compact, and arguably natural way. We compare the estimates of case-based learning to other learning models (reinforcement learning and self-tuned experience weighted attraction learning) while using in-sample and out-of-sample measures. We find evidence that case-based learning explains these data better than the other models based on both in-sample and out-of-sample measures. Additionally, the case-based specification estimates how factors determine the salience of past experiences for the agents. We find that, in constant sum games, opposing players’ behavior is more important than recency and, in non-constant sum games, the reverse is true.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating Case-Based Learning

Guilfoos

Pape

2020

Games

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“…Second, one single parameter ω 1 is hardly capable of successfully describing the dynamics of different lags, whereas the addition of further parameters for each lags counteracts to the desired model simplicity. Further possible specifications for the similarity function can be found in Golosnoy and Okhrin (2008), Guerdjikova (2008) and Lieberman (2010).…”

Section: Saturdaymentioning

confidence: 99%

Forecasting volatility with empirical similarity and Google Trends

Hamid,

Heiden

2015

Journal of Economic Behavior & Organization

116

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“…Guerdjikova () specifies conditions under which this result also holds for a sequence of problems with a lower degree of similarity.…”

mentioning

confidence: 99%

Reason, Intuition, and Time

Sahm

Weizsäcker

2015

Manage. Decis. Econ.

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show abstract

Case-based learning with different similarity functions

Cited by 16 publications

References 20 publications

Estimating Case-Based Learning

Estimating Case-Based Learning

Forecasting volatility with empirical similarity and Google Trends

Reason, Intuition, and Time

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