Predicting Human Choices Under Risk and Uncertainty Using Behavioral Theories and Machine Learning

Core Concepts

Combining behavioral theories and machine learning techniques can provide highly accurate predictions of human choice behavior under risk and uncertainty.

Abstract

The paper introduces BEAST-GB, a hybrid model that integrates the behavioral theory BEAST with machine learning techniques to predict human decision-making under risk and uncertainty. The key insights are:

BEAST-GB, which combines BEAST's behavioral insights with an Extreme Gradient Boosting algorithm, achieves state-of-the-art predictive performance across multiple large datasets of human risky choice. It outperforms both purely behavioral models and purely data-driven neural networks.

The behavioral features derived from BEAST, especially its direct prediction as a feature, are crucial for BEAST-GB's superior performance, even when the training data is large. This highlights the continued relevance of behavioral theory in the presence of abundant data.

BEAST-GB displays robust domain generalization capabilities, accurately predicting choice behavior in new experimental contexts that it was not trained on. This suggests its usefulness captures general choice tendencies beyond just fitting idiosyncratic patterns.

The success of the hybrid BEAST-GB model underscores the value of integrating domain-specific behavioral theories with machine learning techniques, combining the strengths of both approaches. This methodological advance has broad implications for modeling decisions in diverse environments.

Stats

"Predicting human decision-making under risk and uncertainty represents a quintessential challenge that spans economics, psychology, and related disciplines."
"Despite decades of research effort, no model can be said to accurately describe and predict human choice even for the most stylized tasks like choice between lotteries."
"BEAST-GB, a novel hybrid model that synergizes behavioral theories, specifically the model BEAST, with machine learning techniques."
"BEAST-GB won the CPC18 choice prediction competition, outperforming purely data-driven neural networks."
"BEAST-GB achieves state-of-the-art performance on the largest publicly available dataset of human risky choice."

Quotes

"Predicting human decision-making under risk and uncertainty represents a quintessential challenge that spans economics, psychology, and related disciplines."
"Despite decades of research effort, no model can be said to accurately describe and predict human choice even for the most stylized tasks like choice between lotteries."
"BEAST-GB, a novel hybrid model that synergizes behavioral theories, specifically the model BEAST, with machine learning techniques."

Key Insights Distilled From

Predicting human decisions with behavioral theories and machine learning

by Ori Plonsky,... at arxiv.org 04-19-2024

https://arxiv.org/pdf/1904.06866.pdf

Deeper Inquiries

How can the insights from BEAST-GB be applied to improve decision-making in real-world domains like healthcare, finance, and environmental management

The insights from BEAST-GB can be applied to improve decision-making in real-world domains like healthcare, finance, and environmental management by enhancing predictive modeling and understanding human behavior under risk and uncertainty. In healthcare, BEAST-GB can be utilized to predict patient choices and treatment outcomes, aiding in personalized medicine and treatment planning. For finance, the model can be used to predict investor behavior, market trends, and risk assessment, leading to better investment strategies and financial decision-making. In environmental management, BEAST-GB can help predict human choices related to sustainability, resource management, and conservation efforts, guiding policy-making and interventions for environmental protection.

What are the limitations of the hybrid approach, and under what conditions might a purely data-driven or a purely theory-driven model be preferable

The hybrid approach, while powerful in integrating behavioral theories with machine learning for predictive modeling, has limitations. One limitation is the complexity of incorporating theoretical insights into the model, which can make it challenging to interpret and apply in real-world scenarios. Additionally, the hybrid approach may require a significant amount of domain expertise to develop and fine-tune the model effectively. Under certain conditions, a purely data-driven model may be preferable when the dataset is large and diverse, and the patterns in the data can be effectively captured without the need for theoretical constraints. On the other hand, a purely theory-driven model may be preferable when the focus is on understanding the underlying mechanisms of human behavior without the influence of data biases or noise.

What other types of domain-specific theories could be fruitfully combined with machine learning to advance predictive modeling in other areas of human behavior and cognition

Other types of domain-specific theories that could be fruitfully combined with machine learning to advance predictive modeling in other areas of human behavior and cognition include cognitive psychology theories, social psychology theories, and neuroscience theories. Cognitive psychology theories can provide insights into cognitive processes such as memory, attention, and decision-making, which can be integrated with machine learning algorithms to predict human behavior more accurately. Social psychology theories can offer valuable information on social influences, group dynamics, and interpersonal relationships, enhancing the predictive power of models in social contexts. Neuroscience theories can provide insights into brain mechanisms underlying behavior, emotions, and decision-making, which can be leveraged to develop more neurologically-informed predictive models. By combining these domain-specific theories with machine learning techniques, researchers can create more robust and comprehensive models for understanding and predicting human behavior in various domains.