The emergence of hyper-altruistic behaviour in conflictual situations

Situations where people have to decide between hurting themselves or another person are at the core of many individual and global conflicts. Yet little is known about how people behave when facing these situations in the lab. Here we report a large experiment in which participants could either take $x$ dollars from another anonymous participant or give $y$ dollars to the same participant. Depending on the treatments, participants could also exit the game without making any decision, but paying a cost. Across different protocols and parameter specifications, we provide evidence of three regularities: (i) when exiting is allowed and costless, subjects tend to exit the game; (ii) females are more likely than males to exit the game, but only when the cost is small; (iii) when exiting is not allowed, altruistic actions are more common than predicted by the dominant economic models. In particular, against the predictions of every dominant economic model, about one sixth of the subjects show hyper-altruistic tendencies, that is, they prefer giving $y$ rather than taking $x>y$. In doing so, our findings shed light on human decision-making in conflictual situations and suggest that economic models should be revised in order to take into account hyper-altruistic behaviour

Learning backward induction: a neural network agent approach

This paper addresses the question of whether neural networks (NNs), a realistic cognitive model of human information processing, can learn to backward induce in a two-stage game with a unique subgame-perfect Nash equilibrium. The NNs were found to predict the Nash equilibrium approximately 70% of the time in new games. Similarly to humans, the neural network agents are also found to suffer from subgame and truncation inconsistency, supporting the contention that they are appropriate models of general learning in humans. The agents were found to behave in a bounded rational manner as a result of the endogenous emergence of decision heuristics. In particular a very simple heuristic socialmax, that chooses the cell with the highest social payoff explains their behavior approximately 60% of the time, whereas the ownmax heuristic that simply chooses the cell with the maximum payoff for that agent fares worse explaining behavior roughly 38%, albeit still significantly better than chance. These two heuristics were found to be ecologically valid for the backward induction problem as they predicted the Nash equilibrium in 67% and 50% of the games respectively. Compared to various standard classification algorithms, the NNs were found to be only slightly more accurate than standard discriminant analyses. However, the latter do not model the dynamic learning process and have an ad hoc postulated functional form. In contrast, a NN agent’s behavior evolves with experience and is capable of taking on any functional form according to the universal approximation theorem.

Is the Welfare State Sustainable? Experimental Evidence on Citizens' Preferences for Redistribution

The sustainability of the welfare state ultimately depends on citizens' preferences for income redistribution. They are elicited through a Discrete Choice Experiment performed in 2008 in Switzerland. Attributes are redistribution as GDP share, its uses (the unemployed, old-age pensioners, people with ill health etc.), and nationality of beneficiary. Estimated marginal willingness to pay (WTP) is positive among those who deem benefits too low, and negative otherwise. However, even those who state that government should reduce income inequality exhibit a negative WTP on average. The major finding is that estimated average WTP is maximum at 21% of GDP, clearly below the current value of 25%. Thus, the present Swiss welfare state does not appear sustainable.Income redistribution; welfare state; sustainability; preferences; willingness to pay; discrete choice experiments

Is the Welfare State Sustainable? Experimental Evidence on Citizens' Preferences for Redistribution

The sustainability of the welfare state ultimately depends on citizens’ preferences for income redistribution. They are elicited through a Discrete Choice Experiment performed in 2008 in Switzerland. Attributes are redistribution as GDP share, its uses (the unemployed, old-age pensioners, people with ill health etc.), and nationality of beneficiary. Estimated marginal willingness to pay (WTP) is positive among those who deem benefits too low, and negative otherwise. However, even those who state that government should reduce income inequality exhibit a negative WTP on average. The major finding is that estimated average WTP is maximum at 21% of GDP, clearly below the current value of 25%. Thus, the present Swiss welfare state does not appear sustainable.income redistribution, welfare state, sustainability, preferences, willingness to pay, discrete choice experiments

Is the Welfare State Sustainable? Experimental Evidence on Citizens’ Preferences for Redistribution

The sustainability of the welfare state ultimately depends on citizens’ preferences for income redistribution. They are elicited through a Discrete Choice Experiment performed in 2008 in Switzerland. Attributes are redistribution as GDP share, its uses (the unemployed, old-age pensioners, people with ill health etc.), and nationality of beneficiary. Estimated marginal willingness to pay (WTP) is positive among those who deem benefits too low, and negative otherwise. However, even those who state that government should reduce income inequality exhibit a negative WTP on average. The major finding is that estimated average WTP is maximum at 21% of GDP, clearly below the current value of 25%. Thus, the present Swiss welfare state does not appear sustainable.Income redistribution, preferences, willingness to pay, welfare state, sustainability, discrete choice experiments

World model learning and inference

Understanding information processing in the brain-and creating general-purpose artificial intelligence-are long-standing aspirations of scientists and engineers worldwide. The distinctive features of human intelligence are high-level cognition and control in various interactions with the world including the self, which are not defined in advance and are vary over time. The challenge of building human-like intelligent machines, as well as progress in brain science and behavioural analyses, robotics, and their associated theoretical formalisations, speaks to the importance of the world-model learning and inference. In this article, after briefly surveying the history and challenges of internal model learning and probabilistic learning, we introduce the free energy principle, which provides a useful framework within which to consider neuronal computation and probabilistic world models. Next, we showcase examples of human behaviour and cognition explained under that principle. We then describe symbol emergence in the context of probabilistic modelling, as a topic at the frontiers of cognitive robotics. Lastly, we review recent progress in creating human-like intelligence by using novel probabilistic programming languages. The striking consensus that emerges from these studies is that probabilistic descriptions of learning and inference are powerful and effective ways to create human-like artificial intelligent machines and to understand intelligence in the context of how humans interact with their world

Economic Well-Being, Social Mobility, and Preferences for Income Redistribution: Evidence from a Discrete Choice Experiment

In this paper, preferences for income redistribution in Switzerland are elicited through a Discrete Choice Experiment (DCE) performed in 2008. In addition to the amount of redistribution as a share of GDP, attributes also included its uses (working poor, the unemployed, old-age pensioners, families with children, people in ill health) and nationality of beneficiary (Swiss, Western European, others). Willingness to pay for redistribution increases with income and education, contradicting the conventional Meltzer-Richard (1981) model. The Prospect of Upward Mobility hypothesis [Hirschman and Rothschild (1973); Benabou and Ok (2001)] receives partial empirical support.Income redistribution, preferences, willingness to pay, discrete choice experiments, stated choice, economic well-being, social mobility

A computational neuroscience perspective on subjective wellbeing within the active inference framework

Understanding and promoting subjective wellbeing (SWB) has been the topic of increasing research, due in part to its potential contributions to health and productivity. To date, the conceptualization of SWB has been grounded within social psychology and largely focused on self-report measures. In this paper, we explore the potentially complementary tools and theoretical perspectives offered by computational neuroscience, with a focus on the active inference (AI) framework. This framework is motivated by the fact that the brain does not have direct access to the world; to select actions, it must instead infer the most likely external causes of the sensory input it receives from both the body and the external world. Because sensory input is always consistent with multiple interpretations, the brain’s internal model must use background knowledge, in the form of prior expectations, to make a “best guess” about the situation it is in and how it will change by taking one action or another. This best guess arises by minimizing an error signal representing the deviation between predicted and observed sensations given a chosen action—quantified mathematically by a variable called free energy (FE). Crucially, recent proposals have illustrated how emotional experience may emerge within AI as a natural consequence of the brain keeping track of the success of its model in selecting actions to minimize FE. In this paper, we draw on the concepts and mathematics in AI to highlight how different computational strategies can be used to minimize FE—some more successfully than others. This affords a characterization of how diverse individuals may adopt unique strategies for achieving high SWB. It also highlights novel ways in which SWB could be effectively improved. These considerations lead us to propose a novel computational framework for understanding SWB. We highlight several parameters in these models that could explain individual and cultural differences in SWB, and how they might inspire novel interventions. We conclude by proposing a line of future empirical research based on computational modelling that could complement current approaches to the study of wellbeing and its improvement