Intention regulates conflicting desires in human decision making

It is an ancient insight that human actions are driven by desires. This insight inspired the formulation that a rational agent acts to maximize expected utility (MEU), which has been widely used in psychology for modeling theory of mind and in artificial intelligence (AI) for controlling machines’ actions. Yet, it's rather unclear how humans act coherently when their desires are complex and often conflicting with each other. Here we show desires do not directly control human actions. Instead, actions are regulated by an intention — a deliberate mental state that commits to a fixed future rather than taking the expected utilities of many futures evaluated by many desires. Our study reveals four behavioral signatures of human intention by demonstrating how human sequential decision-making deviates from the optimal policy based on MEU in a navigation task: “Disruption resistance” as the persistent pursuit of an original intention despite an unexpected change has made that intention suboptimal; “Ulysses-constraint of freedom” as the proactive constraint of one’s freedom by avoiding a path that could lead to many futures, similar to Ulysses’s self-binding to resist the temptation of the Siren’s song; “Enhanced legibility” as an active demonstration of intention by choosing a path whose destination can be promptly inferred by a third-party observer; “Temporal leap” as committing to a distant future even before reaching the proximal one. Our results showed how the philosophy of intention can lead to discoveries of human decision-making, which can also be empirically compared with AI algorithms. The findings showing that to define a theory of mind, intention should be highlighted as a distinctive mental state in between desires and actions, for quarantining conflicting desires from the execution of actions.

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Mind Theory and the Role of Financial Decision and Process Role of Optogenetics

Financial Markets Institutions and Risks ◽

10.21272/fmir.4(1).40-50.2020 ◽

2020 ◽

Vol 4 (1) ◽

pp. 40-50

Author(s):

Ana Njegovanović

Keyword(s):

Decision Making ◽

Theory Of Mind ◽

Financial Market ◽

Behavioral Finance ◽

Mental State ◽

Objective Assessment ◽

Financial Decision Making ◽

Functional Relationships ◽

Financial Decision

This paper is devoted to the study of functional relationships between behavioral finance, in particular when making decisions in the financial market, and the theory of reason and optogenetics. The purpose of this paper is to analyze the interaction of financial decision-making processes with the key principles of the mental state model (theory of mind) and define the role of optogenetics. The author notes that the use of the theory of reason in behavioral finance allows us to consider the key characteristics of the mental state of the subject of economic relations (thoughts, perceptions, desires, intentions, feelings have an internal mentalistic and experimental content). The author notes that decision-making at any level characterizes the complex network of scientific industries that allow us to understand the complexity of financial decision-making and the role and significance of the laws of thermodynamics and entropy. Modeling neural networks (based on the experimental approach), the paper presents the results of research in the context of analyzing behavioral changes in our brain under the following scenarios: at the stage of awareness of certain processes; if we participate (or do not) participate in these processes. The following conclusions are made in the paper: for the normal states of anxiety, the greatest number of possible configurations of interactions between brain networks, which represent the highest values of entropy is characteristic. These results are obtained from the study of a small number of participants in the experiment, but give an objective assessment and understanding of the complexity of the research and the guidance that include a scientific basis in the process of solving problems in the financial sphere (as an example: when trading in the financial market). Keywords: behavioral finance; theory of mind, financial decision making, optogenetics.

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Automatic discovery of interpretable planning strategies

Machine Learning ◽

10.1007/s10994-021-05963-2 ◽

2021 ◽

Author(s):

Julian Skirzyński ◽

Frederic Becker ◽

Falk Lieder

Keyword(s):

Decision Making ◽

Reinforcement Learning ◽

Information Acquisition ◽

Decision Aids ◽

Decision Rules ◽

Sequential Decision ◽

Behavioral Experiments ◽

Good Decision ◽

Human Decision ◽

Planning Strategies

AbstractWhen making decisions, people often overlook critical information or are overly swayed by irrelevant information. A common approach to mitigate these biases is to provide decision-makers, especially professionals such as medical doctors, with decision aids, such as decision trees and flowcharts. Designing effective decision aids is a difficult problem. We propose that recently developed reinforcement learning methods for discovering clever heuristics for good decision-making can be partially leveraged to assist human experts in this design process. One of the biggest remaining obstacles to leveraging the aforementioned methods for improving human decision-making is that the policies they learn are opaque to people. To solve this problem, we introduce AI-Interpret: a general method for transforming idiosyncratic policies into simple and interpretable descriptions. Our algorithm combines recent advances in imitation learning and program induction with a new clustering method for identifying a large subset of demonstrations that can be accurately described by a simple, high-performing decision rule. We evaluate our new AI-Interpret algorithm and employ it to translate information-acquisition policies discovered through metalevel reinforcement learning. The results of three large behavioral experiments showed that providing the decision rules generated by AI-Interpret as flowcharts significantly improved people’s planning strategies and decisions across three different classes of sequential decision problems. Moreover, our fourth experiment revealed that this approach is significantly more effective at improving human decision-making than training people by giving them performance feedback. Finally, a series of ablation studies confirmed that our AI-Interpret algorithm was critical to the discovery of interpretable decision rules and that it is ready to be applied to other reinforcement learning problems. We conclude that the methods and findings presented in this article are an important step towards leveraging automatic strategy discovery to improve human decision-making. The code for our algorithm and the experiments is available at https://github.com/RationalityEnhancement/InterpretableStrategyDiscovery.

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Theory of Mind and Moral Decision-Making in the Context of Autism Spectrum Disorder

10.1101/2021.03.13.21253459 ◽

2021 ◽

Author(s):

Jessica Ellen Ringshaw ◽

Katie Hamilton ◽

Susan Malcolm-Smith

Keyword(s):

Decision Making ◽

Autism Spectrum Disorder ◽

Theory Of Mind ◽

Cognitive Empathy ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Social Impairment ◽

Third Party ◽

Moral Decision ◽

Moral Decision Making

AbstractSocial impairment in Autism Spectrum Disorder (ASD) has been linked to Theory of Mind (ToM) deficits. However, little research has investigated the relationship between ToM and moral decision-making in children with ASD. This study compared moral decision-making and ToM between aggregate-matched ASD and neurotypical boys (n=38 per group; aged 6-12). In a third-party resource allocation task manipulating recipient merit, wealth and health, neurotypical children allocated significantly more resources to the morally deserving recipient, suggesting equitable allocation. A comparatively larger portion of the ASD group allocated equally. ToM emerged as a predictor of moral decision-making. We suggest that ToM (cognitive empathy) deficits may underly atypical moral decision-making in ASD by limiting the integration of empathic arousal (affective empathy) with moral information.

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