Autoshaped choice in artificial neural networks: Implications for behavioral economics and neuroeconomics

Burgos, José E.; García-Leal, Óscar

doi:10.1016/j.beproc.2015.01.010

Cited by 8 publications

(5 citation statements)

References 38 publications

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“…Some studies discuss how subtle nudges can influence consumer behavior, often without the consumer's knowledge, and how cognitive biases can affect decision-making in the presence of these nudges [63]. Other articles investigate how machine learning techniques, such as SVM and feature selection, can be applied to neuromarketing research to gain insights into consumer behavior and preferences [64], examine how AI and artificial neural networks can help improve our understanding of decision-making processes in the context of neuroeconomics [65], the use of inverse reinforcement learning (IRL) to explain user behavior on YouTube [66], and the use of prospect theory and hybrid machine learning, which combines traditional econometric models with machine learning algorithms to analyze the impact of macroeconomic factors such as inflation and economic growth on exchange rates [67].…”

Section: ) Thematic Structure Through Co-word Analysismentioning

confidence: 99%

“…• Customer Behavior • Technology Adoption Green cluster (54 items): [64], artificial neural networks to enhance the understanding of economic decisionmaking processes [65], and data mining algorithms to personalize tariff plans and forecast customer engagement behavior [74] [75]. • On the other hand, behavioral finance-related topics, especially investor behavioral and stock market studies, are on the rise.…”

Section: B Key Findings For the Science Mapping And Network Analysismentioning

confidence: 99%

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Artificial Intelligence and Behavioral Economics: A Bibliographic Analysis of Research Field

Aoujil,

Hanine,

Flores

et al. 2023

IEEE Access

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Behavioral economics and artificial intelligence (AI) have been two rapidly growing fields of research over the past few years. While behavioral economics aims to combine concepts from psychology, sociology, and neuroscience with classical economic thoughts to understand human decision-making processes in the complex economic environment, AI on the other hand, focuses on creating intelligent machines that can mimic human cognitive abilities such as learning, problem-solving, decision-making, and language understanding. The intersection of these two fields has led to thrilling research theories and practical applications. This study provides a bibliometric analysis of the literature on AI and behavioral economics to gain insight into research trends in this field. We conducted this bibliometric analysis using the Web of Science database on articles published between 2012 and 2022 that were related to AI and behavioral economics. VOSviewer and Bibliometrix R package were utilized to identify influential authors, journals, institutions, and countries in the field. Network analysis was also performed to identify the main research themes and their interrelationships. The analysis revealed that the number of publications on AI and behavioral economics has been increasing steadily over the past decade. We found that most studies focused on customer and consumer behavior, including topics such as decision-making under uncertainty, neuroeconomics, and behavioral game theory, combined mainly with machine learning and deep learning techniques. We also identified several emerging themes, including the use of AI in nudging and prospect theory in behavioral finance, as well as undeveloped themes such as AI-driven behavioral macroeconomics. The findings suggests that there is a need for more interdisciplinary collaboration between researchers in behavioral economics and AI. We also suggest that future research on AI and behavioral economics further consider the ethical implications of using AI and behavioral insights in decision-making. This study can serve as a valuable resource for researchers interested in AI and behavioral economics.INDEX TERMS Artificial intelligence; behavioral economics; behavioral finance; consumer behavior; investor behavior; decision making; neuroeconomics; machine learning; bibliometric analysis I. INTRODUCTIONB EHAVIORAL economics is a subfield of economics that examines the decision-making processes of individuals and groups and how they affect economic outcomes. It is an interdisciplinary field that combines elements of psychology, sociology, neuroscience, and economics to provide a more complete understanding of human behavior in the marketplace [1]. It differs from classical economics, which assumes that individuals are rational and always make decisions that maximize their utility. In contrast, behavioral economics

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Section: ) Thematic Structure Through Co-word Analysismentioning

confidence: 99%

Section: B Key Findings For the Science Mapping And Network Analysismentioning

confidence: 99%

Artificial Intelligence and Behavioral Economics: A Bibliographic Analysis of Research Field

Aoujil,

Hanine,

Flores

et al. 2023

IEEE Access

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“…Even so, those models do not account for the multifaceted circuitry (e.g., including the LHb-VTA-mPFC patway) of this structure which, we argue, is highly relevant for an utter understanding of its role in learning. Lack of inclusion of the LHb into neural network modeling (e.g., Burgos and García-Leal, 2015;Collins and Frank, 2014;O'Reilly, Russin and Herd, 2019;Sutton and Barto, 2018) may be partly due to an overemphasis in performance over learning of inhibitory control.…”

Section: Directions For Moving the Field Forwardmentioning

confidence: 99%

The Role of the Lateral Habenula in Inhibitory Learning from Reward Omission

Sosa

Mata-Luévanos²,

Buenrostro-Jáuregui³

2021

eNeuro

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The lateral habenula (LHb) is a phylogenetically primitive brain structure that plays a key role in learning to inhibit distinct responses to specific stimuli. This structure is activated by primary aversive stimuli, cues predicting an imminent aversive event, unexpected reward omissions, and cues associated with the omission of an expected reward. The most widely described effect of LHb activation is acutely suppressing midbrain dopaminergic signaling. However, recent studies have identified multiple means by which the LHb foster this effect as well as other mechanisms of action. These findings reveal the complex nature of LHb function. The present paper reviews the role of this structure in learning from reward omission experiences. We approach this topic from the perspective of computational models of behavioral change that account for inhibitory learning to frame key findings. Such findings are drawn from recent behavioral neuroscience studies that use novel brain imaging, stimulation, ablation, and reversible inactivation techniques. Further research and conceptual work are needed to clarify the nature of the processes related to updating motivated behavior in which the LHb is involved. As yet, there is little understanding of whether such mechanisms are parallel or complementary to the well-known modulatory function of the more recently evolved prefrontal cortex.

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“…At the intersection of operant with Pavlovian conditioning, the model also simulates autoshaping, automaintenance (Burgos, 2007), and autoshaped choice (Burgos & García-Leal, 2015).…”

Section: The Modelmentioning

confidence: 99%

Misbehavior in a Neural Network Model

Burgos

2015

IJCP

Self Cite

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This paper describes a neural network account of misbehavior with an extant neural network model of conditioning. The model makes no distinction between learning (weight-change mechanisms) in operant and Pavlovian conditioning, but preserves the standard behavioral distinctions between types of stimuli, responses, and contingencies, with connectionist interpretations of some possible neuroanatomical substrates. Misbehavior has been traditionally conceived as a species-specific response R* that is unnecessary for a biologically significant reward S* but interferes with another response R that is necessary for S*. Misbehavior thus conceived has been explained as interfering Pavlovian conditioned responding. Three four-layer feedforward neural networks were designed to differ only in their output layers, as a connectionist interpretation of three hypothetical operant-Pavlovian relations in misbehavior, namely, interference (Pavlovian output to operant output lateral inhibitory connection), compatibility (Pavlovian output to operant output lateral excitatory connection), and independence (no lateral connection in the output layer). These relations are proposed as neural-network interpretations of neuroanatomical substrates of conditioning with three biologically significant stimuli, namely, food, water, and sexual mate, respectively. Each network first received pairings of contextual cues with its respective S*, to simulate pretraining with such stimuli. Then, networks received operant contingencies where S* was paired with the same contextual cues, as well as cues from a token dependently on R responding, defined as a minimal R activation of 0.5. Networks showed substantial misbehavior (qua conditioned R* responding) that interfered with R to different extents, food causing the most, sexual mate the least interference. Limitations, future directions, and implications for biological constraints and the generality of learning are discussed.

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Autoshaped choice in artificial neural networks: Implications for behavioral economics and neuroeconomics

Cited by 8 publications

References 38 publications

Artificial Intelligence and Behavioral Economics: A Bibliographic Analysis of Research Field

Artificial Intelligence and Behavioral Economics: A Bibliographic Analysis of Research Field

The Role of the Lateral Habenula in Inhibitory Learning from Reward Omission

Misbehavior in a Neural Network Model

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