A Quantum-Conceptual Explanation of Violations of Expected Utility in Economics

The expected utility hypothesis is one of the building blocks of classical economic theory and founded on Savage's Sure-Thing Principle. It has been put forward, e.g. by situations such as the Allais and Ellsberg paradoxes, that real-life situations can violate Savage's Sure-Thing Principle and hence also expected utility. We analyze how this violation is connected to the presence of the 'disjunction effect' of decision theory and use our earlier study of this effect in concept theory to put forward an explanation of the violation of Savage's Sure-Thing Principle, namely the presence of 'quantum conceptual thought' next to 'classical logical thought' within a double layer structure of human thought during the decision process. Quantum conceptual thought can be modeled mathematically by the quantum mechanical formalism, which we illustrate by modeling the Hawaii problem situation, a well-known example of the disjunction effect, and we show how the dynamics in the Hawaii problem situation is generated by the whole conceptual landscape surrounding the decision situation.Comment: 9 pages, no figure

Learning to Generate Ambiguous Sequences

In this paper, we experiment with methods for obtaining binary sequences with a random probability mass function and with low autocorrelation and use it to generate ambiguous outcomes. Outputs from a neural network are mixed and shuffled, resulting in binary sequences whose probability mass function is non-convergent, constantly moving and changing. Empirical comparison with algorithms that generate ambiguity shows that the sequences generated by the proposed method have a significantly lower serial dependence. Therefore, the method is useful in scenarios where observes can see and record the outcome of each draw sequentially, by hindering the ability to make useful statistical inferences

Reasons and Means to Model Preferences as Incomplete

Literature involving preferences of artificial agents or human beings often assume their preferences can be represented using a complete transitive binary relation. Much has been written however on different models of preferences. We review some of the reasons that have been put forward to justify more complex modeling, and review some of the techniques that have been proposed to obtain models of such preferences

How brains make decisions

This chapter, dedicated to the memory of Mino Freund, summarizes the Quantum Decision Theory (QDT) that we have developed in a series of publications since 2008. We formulate a general mathematical scheme of how decisions are taken, using the point of view of psychological and cognitive sciences, without touching physiological aspects. The basic principles of how intelligence acts are discussed. The human brain processes involved in decisions are argued to be principally different from straightforward computer operations. The difference lies in the conscious-subconscious duality of the decision making process and the role of emotions that compete with utility optimization. The most general approach for characterizing the process of decision making, taking into account the conscious-subconscious duality, uses the framework of functional analysis in Hilbert spaces, similarly to that used in the quantum theory of measurements. This does not imply that the brain is a quantum system, but just allows for the simplest and most general extension of classical decision theory. The resulting theory of quantum decision making, based on the rules of quantum measurements, solves all paradoxes of classical decision making, allowing for quantitative predictions that are in excellent agreement with experiments. Finally, we provide a novel application by comparing the predictions of QDT with experiments on the prisoner dilemma game. The developed theory can serve as a guide for creating artificial intelligence acting by quantum rules.Comment: Latex file, 20 pages, 3 figure

What factors are associated with recent intimate partner violence? findings from the WHO multi-country study on women's health and domestic violence

<p>Abstract</p> <p>Background</p> <p>Intimate partner violence (IPV) against women is a global public health and human rights concern. Despite a growing body of research into risk factors for IPV, methodological differences limit the extent to which comparisons can be made between studies. We used data from ten countries included in the WHO Multi-country Study on Women's Health and Domestic Violence to identify factors that are consistently associated with abuse across sites, in order to inform the design of IPV prevention programs.</p> <p>Methods</p> <p>Standardised population-based household surveys were done between 2000 and 2003. One woman aged 15-49 years was randomly selected from each sampled household. Those who had ever had a male partner were asked about their experiences of physically and sexually violent acts. We performed multivariate logistic regression to identify predictors of physical and/or sexual partner violence within the past 12 months.</p> <p>Results</p> <p>Despite wide variations in the prevalence of IPV, many factors affected IPV risk similarly across sites. Secondary education, high SES, and formal marriage offered protection, while alcohol abuse, cohabitation, young age, attitudes supportive of wife beating, having outside sexual partners, experiencing childhood abuse, growing up with domestic violence, and experiencing or perpetrating other forms of violence in adulthood, increased the risk of IPV. The strength of the association was greatest when both the woman and her partner had the risk factor.</p> <p>Conclusions</p> <p>IPV prevention programs should increase focus on transforming gender norms and attitudes, addressing childhood abuse, and reducing harmful drinking. Development initiatives to improve access to education for girls and boys may also have an important role in violence prevention.</p

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating

Perceptions and Experiences of Research Participants on Gender-Based Violence Community Based Survey: Implications for Ethical Guidelines

OBJECTIVE: To explore how survey respondents perceived their experiences and the impact of participating in a survey, and to assess adverse consequences resulting from participation. DESIGN: Qualitative study involving purposefully selected participants who had participated in a household-based survey. METHODS: This qualitative study was nested within a survey that investigated the prevalence of gender-based violence perpetration and victimization with adult men and women in South Africa. 13 male- and 10 female-in-depth interviews were conducted with survey respondents. RESULTS: A majority of informants, without gender-differences, perceived the survey interview as a rare opportunity to share their adverse and or personal experiences in a 'safe' space. Gender-differences were noted in reporting perceptions of risks involved with survey participation. Some women remained fearful after completing the survey, that should breach of confidentiality or full survey content disclosure occur, they may be victimized by partners as a punishment for survey participation without men's approval. A number of informants generally discussed their survey participation with others. However, among women with interpersonal violence history or currently in abusive relationships, full survey content disclosure was done with fear; the partner responses were negative, and few women reported receiving threatening remarks but none reported being assaulted. In contrast no man reported adverse reaction by others. Informants with major life adversities reported that the survey had made them to relive the experiences causing them sadness and pain at the time. No informant perceived the survey as emotionally harmful or needed professional support because of survey questions. Rather the vast majority perceived benefit from survey participation. CONCLUSION: Whilst no informant felt answering the survey questions had caused them emotional or physical harm, some were distressed and anxious, albeit temporarily. Research protocols need to put in place safeguards where appropriate so that this group receives support and protection

Unifying Decision-Making: a Review on Evolutionary Theories on Rationality and Cognitive Biases

In this paper, we make a review on the concepts of rationality across several different fields, namely in economics, psychology and evolutionary biology and behavioural ecology. We review how processes like natural selection can help us understand the evolution of cognition and how cognitive biases might be a consequence of this natural selection. In the end we argue that humans are not irrational, but rather rationally bounded and we complement the discussion on how quantum cognitive models can contribute for the modelling and prediction of human paradoxical decisions