Gender effects for loss aversion: yes, no, maybe?

Gender effects in risk taking have attracted much attention by economists, and remain debated. Loss aversion—the stylized finding that a given loss carries substantially greater weight than a monetarily equivalent gain—is a fundamental driver of risk aversion. We deploy four definitions of loss aversion commonly used in the literature to investigate gender effects. Even though the definitions only differ in subtle ways, we find women to be more loss averse than men according to one definition, while another definition results in no gender differences, and the remaining two definitions point to women being less loss averse than men. Conceptually, these contradictory effects can be organized by systematic measurement error resulting from model mis-specifications relative to the true underlying decision process

Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow

We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow. Our library enables the plug-and-play of popular TensorFlow-based models within sequential decision-making loops, e.g. Gaussian processes from GPflow or GPflux, or neural networks from Keras. This modular mindset is central to the package and extends to our acquisition functions and the internal dynamics of the decision-making loop, both of which can be tailored and extended by researchers or engineers when tackling custom use cases. Trieste is a research-friendly and production-ready toolkit backed by a comprehensive test suite, extensive documentation, and available at https://github.com/secondmind-labs/trieste

Project files accompanying the paper "An empirical evaluation of active inference in multi-armed bandits"

Preprint can be found at: https://arxiv.org/abs/2101.0869

The collective dynamics of sequential search in markets for cultural products

The collective dynamics of sequential search in markets for cultural product

Not everything looks like a nail: Learning to select appropriate decision strategies in multiple environments

How do people choose which decision strategy to use? When facing single tasks, research shows that people can learn to select appropriate strategies. However, what happens when, as is typical outside the psychological laboratory, they face multiple tasks? Participants were presented with two interleaved decision tasks, one from a nonlinear environment, the other from a linear environment. The environments were initially unknown and participants had to learn their properties. Through cognitive modeling, we examined the types of strategies adopted in both tasks. Based on out of sample predictions, most participants adopted a cue-based strategy in the linear environment and an exemplar-based strategy in the nonlinear environment. A context-sensitive reinforcement learning model accounts for this process. Thus, people associated different strategies to different types of environments through a trial-and-error type of process, and learned to flexibly switch between the strategies as needed. This evidence further supports the strategy selection approach to decision making which assumes that people pick and apply strategies available to them according to task demands

No adaptive strategy selection without outcome feedback

<p>This work presents results from a project that aimed to replicate results from Dieckmann & Rieskamp (2007) article involving strategy selection in decision making, and to point out some crucial flaws in their experimental design. It was presented at CogSci 2013 and SPUDM 2013.</p

Human behavior in contextual multi-armed bandit problems

In real-life decision environments people learn from their direct experience with alternative courses of action. Yet they can accelerate their learning by using functional knowledge about the features characterizing the alternatives. We designed a novel contextual multi-armed bandit task where decision makers chose repeatedly between multiple alternatives characterized by two informative features. We compared human behavior in this contextual task with a classic multi-armed bandit task without feature information. Behavioral analysis showed that participants in the contextual bandit task used the feature information to direct their exploration of promising alternatives. Ex post, we tested participants’ acquired functional knowledge in one-shot multi-feature choice trilemmas. We compared a novel function-learning-based reinforcement learning model to a classic reinforcement learning. Although reinforcement learning models predicted behavior better in the learning phase, the new models did better in predicting the trilemma choice