Neural Networks in Economics

Herbrich, Ralf; Keilbach, Max; Graepel, Thore; Bollmann-Sdorra, Peter; Obermayer, Klaus

doi:10.1007/978-1-4615-5029-7_7

Cited by 45 publications

(24 citation statements)

References 45 publications

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“…Our study reveals neural computations by which observations of pairwise “contests” are used to update estimates of individuals’ power within a hierarchy and provides compelling evidence that a Bayesian inference scheme, which has certain parallels with the Trueskill ratings system (Herbrich et al., 2006) used in large-scale multiplayer games, underlies this process, rather than a simpler RL mechanism. At the same time, our results, in ascribing a specific role to the MPFC in the learning of one’s own social hierarchy under tightly controlled experimental conditions, invigorate the debate concerning whether self-relevant information is indeed afforded a unique representational status in the brain.…”

Section: Discussionmentioning

confidence: 65%

Computations Underlying Social Hierarchy Learning: Distinct Neural Mechanisms for Updating and Representing Self-Relevant Information

Kumaran

Banino

Blundell

et al. 2016

Neuron

133

136

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SummaryKnowledge about social hierarchies organizes human behavior, yet we understand little about the underlying computations. Here we show that a Bayesian inference scheme, which tracks the power of individuals, better captures behavioral and neural data compared with a reinforcement learning model inspired by rating systems used in games such as chess. We provide evidence that the medial prefrontal cortex (MPFC) selectively mediates the updating of knowledge about one’s own hierarchy, as opposed to that of another individual, a process that underpinned successful performance and involved functional interactions with the amygdala and hippocampus. In contrast, we observed domain-general coding of rank in the amygdala and hippocampus, even when the task did not require it. Our findings reveal the computations underlying a core aspect of social cognition and provide new evidence that self-relevant information may indeed be afforded a unique representational status in the brain.

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Section: Discussionmentioning

confidence: 65%

Computations Underlying Social Hierarchy Learning: Distinct Neural Mechanisms for Updating and Representing Self-Relevant Information

Kumaran

Banino

Blundell

et al. 2016

Neuron

133

136

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“…SeeHerbrich et al (1999) for a classification and a short review. 7 See for examplePerez (2006).8 The architecture of a multilayer perceptron will be explained in the following sections.…”

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confidence: 99%

Predicting sovereign debt crises using artificial neural networks: A comparative approach

Fioramanti¹

2008

Journal of Financial Stability

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“…Rumelhart and McClelland, 1986), have been applied to a large variety of problems ranging from pattern recognition to transportation (Himanen et al, 1998;Reggiani et al, 2000). For an historical review of the NN methodology we refer, among others, to Taylor (1997); for an overview of NN applications in the economic field we refer to Herbrich et al (1999).…”

Section: Neural Network Methodologymentioning

confidence: 99%

Multicriteria Analysis of Neural Network Forecasting Models: An Application to German Regional Labour Markets

et al. 2002

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This paper develops a flexible multi-dimensional assessment method for the comparison of different statistical-econometric techniques based on learning mechanisms with a view to analys ing and forecasting regional labour markets.The aim of this paper is twofold. A first major objective is to explore the use of a standard choice tool, namely Multicriteria Analysis (MCA), in order to cope with the intrinsic methodological uncertainty on the choice of a suitable statistical econometric learning technique for regional labour market analysis. MCA is applied here to support choices on the performance of various models -based on classes of Neural Network (NN) techniques that serve to generate employment forecasts in West Germany at a regional/district level. A second objective of the paper is to analyse the methodological potential of a blend of approaches (NN-MCA) in order to extend the analysis framework to other economic research domains, where formal models are not available, but where a variety of statistical data is present. The paper offers a basis for a more balanced judgement of the performance of rival statistical tests. Need for a New Statistical Test FrameworkThe modern information age has dramatically increased the scientific potential to handle large scale data sets. Simulation of 'big models' has become a popular modelling activity, as the computational capacity of modern computers has exhibited a sky-rocketing pathway. The good old days of statistics and econometrics, which were for researchers a 'serious play to estimate one model a day' using standard ordinary least squares techniques, have passed by. We are now able to estimate an enormous range of model specifications under different background conditions, with a large set of sensitivity tests, and with the help of different

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Neural Networks in Economics

Cited by 45 publications

References 45 publications

Computations Underlying Social Hierarchy Learning: Distinct Neural Mechanisms for Updating and Representing Self-Relevant Information

Computations Underlying Social Hierarchy Learning: Distinct Neural Mechanisms for Updating and Representing Self-Relevant Information

Predicting sovereign debt crises using artificial neural networks: A comparative approach

Multicriteria Analysis of Neural Network Forecasting Models: An Application to German Regional Labour Markets

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