Games under Ambiguous Payoffs and Optimistic Attitudes

In real-life games, the consequence or payoff of a strategy profile and a player's belief about the consequence of a strategy profile are often ambiguous, and players may have different optimistic attitudes with respect to a strategy profile. To handle this problem, this paper proposes a decision rule using the Hurwicz criterion and Dempster-Shafer theory. Based on this rule, we introduce a new kind of games, called ambiguous games, and propose a solution concept that is appropriate for this sort of games. Moreover, we also study how the beliefs regarding possible payoffs and optimistic attitudes may affect the solutions of such a game. To illustrate our model, we provide an analysis of a scenario concerning allocating resource of defending and attacking in military contexts

Essays on behavioural economics: Uncovering drivers of altruistic behaviour

This Ph.D. thesis aims to study the economic foundations of prosocial behaviour in multiple significant ways. First, it emphases that altruistic behaviour can be the result of individual differences in people when using survey experiments, but also in nations when analysing information at an aggregate level. Second, it also states that altruism appears more prominently when more options are given for decision-makers to choose among alternatives to donate. For that purpose, I present a broad literature review and four empirical essays that provide new evidence on these particular topics. The first essay makes an overall analysis on altruism at a global scale using a database from The World Bank and The World Happiness report for the period 2020. The empirical analysis is conducted using cross-sectional country data from a sample of 128 worldwide countries in the 6 continents. The results suggest that nations which exhibit higher generosity levels are also quite distinct from the others, such as in the level of economic development, in some socio-demographic variables and cultural dimensions. The other three essays are based on the collection of experimental survey data aiming at identifying new factors that may explain generous behaviour in individuals. Specifically, the second tries to stablish a relationship between free will beliefs and giving, the third relates cognitive skills with strategic thinking abilities and the last one studies how the number of options available affects giving. The results suggest that higher free will beliefs have a statistically significant effect on generous concerns. Personal cognitive skills and strategic thinking abilities also have a relationship with giving. However, the former has a negative influence while the latter is positive. Finally, in the last essay, I observe that generosity increases when more recipient options are available and this effect is statistically significant, as well. This thesis contributes to our understanding of prosocial behaviour in terms of individual and country characteristics that are correlated with altruistic behaviour.Esta tese de doutoramento visa estudar as bases económicas do comportamento pro-social de várias formas distintas: Em primeiro lugar, enfatiza que o comportamento altruísta pode ser o resultado de diferenças individuais em seres humanos, quando se recolhem dados através de inquéritos, mas também em países, quando se analisa informação a um nível agregado. Em segundo lugar, demonstra que o comportamento altruísta emerge de uma forma mais notória quando são dadas mais opções às pessoas para escolherem entre alternativas para doar. Para o efeito, apresento uma revisão da literatura generalizada e quatro ensaios empíricos que sugerem novas evidências sobre estes tópicos, em particular. O primeiro ensaio faz uma análise sobre o altruísmo à escala global utilizando dados do Banco Mundial e do relatório The World Happiness Report referente ao período de 2020. A análise empírica é conduzida utilizando dados de uma amostra de 128 países em 6 continentes. Os resultados sugerem que as nações que apresentam níveis de generosidade mais elevados são também bastante distintas em relação às restantes, nomeadamente ao nível do desenvolvimento económico, na vertente sociodemográfica e ainda culturalmente. Os outros três ensaios baseiam-se na recolha de dados através de inquéritos com o objetivo de identificar novos fatores que possam explicar o comportamento pro-social em indivíduos. Especificamente, o segundo tenta estabelecer uma relação entre crenças no livre-arbítrio e generosidade, o terceiro com capacidades cognitivas/estratégicas e o último com o número de opções disponíveis para doação. Os resultados sugerem que as pessoas que possuem crenças mais robustas no livre-arbítrio revelam também ter maiores tendências generosas. Os resultados sugerem ainda que as competências cognitivas e as capacidades de pensamento estratégico têm também uma relação com o altruísmo. No entanto, o primeiro fator tem uma influência negativa enquanto o segundo positiva. Finalmente, no último ensaio, foi observado que a generosidade aumenta quando estão disponíveis mais opções para doar. Globalmente, esta tese contribui para aumentar a nossa compreensão do comportamento pro-social em termos das características individuais que lhe estão correlacionadas

Modelling religious signalling

The origins of human social cooperation confound simple evolutionary explanation. But from Darwin and Durkheim onwards, theorists (anthropologists and sociologists especially) have posited a potential link with another curious and distinctively human social trait that cries out for explanation: religion. This dissertation explores one contemporary theory of the co-evolution of religion and human social cooperation: the signalling theory of religion, or religious signalling theory (RST). According to the signalling theory, participation in social religion (and its associated rituals and sanctions) acts as an honest signal of one's commitment to a religiously demarcated community and its way of doing things. This signal would allow prosocial individuals to positively assort with one another for mutual advantage, to the exclusion of more exploitative individuals. In effect, the theory offers a way that religion and cooperation might explain one another, but which that stays within an individualist adaptive paradigm. My approach is not to assess the empirical adequacy of the religious signalling explanation or contrast it with other explanations, but rather to deal with the theory in its own terms - isolating and fleshing out its core commitments, explanatory potential, and limitations. The key to this is acknowledging the internal complexities of signalling theory, with respect to the available models of honest signalling and the extent of their fit (or otherwise) with religion as a target system. The method is to take seriously the findings of formal modelling in animal signalling and other disciplines, and to apply these (and methods from the philosophy of biology more generally) to progressively build up a comprehensive picture of the theory, its inherent strengths and weaknesses. The first two chapters outline the dual explanatory problems that cooperation and religion present for evolutionary human science, and surveys contemporary approaches toward explaining them. Chapter three articulates an evolutionary conception of the signalling theory, and chapters four to six make the case for a series of requirements, limitations, and principles of application. Chapters seven and eight argue for the value of formal modelling to further flesh out the theory's commitments and potential and describe some simple simulation results which make progress in this regard. Though the inquiry often problematizes the signalling theory, it also shows that it should not be dismissed outright, and that it makes predictions which are apt for empirical testing

Individual decision making, reinforcement learning and myopic behaviour

Individuals use their cognitive abilities to make decisions, with the ultimate goal of improving their status. Decisions outcomes are used to learn the association between the decisions which lead to good results and those resulting in punishing outcomes. These associations might not be easily inferable because of environmental complexity or noisy feedback. Tasks in which outcomes probabilities are known are termed “decisions under risk”. Researchers have consistently showed that people are risk averse when choosing among options featuring gains, while they are risk seeking when making decisions about options featuring losses. When the probabilities of the options are not clearly stated the task is known as “decisions under ambiguity”. In this type of task individuals face an exploration-exploitation trade off: to maximise their profit they need to choose the best option but at the same time they need to discover which option leads to the best outcome by trial-and-error. The process of knowledge acquisition by interaction with the environment is called adaptive learning. Evidence from literature points in the direction of unskilled investors behaviour being consistent with naive reinforcement learning, simply adjusting their preference for which option to choose based on its recent outcomes. Experimental data from a binary choice task and a quasi-field scenario is used to test a combination of Reinforcement Learning and Prospect Theory. Both the investigations include reinforcement learning models featuring specific parameters which can be tuned to describe individual learning decision-making strategies. The first part is focused on integrating the two computational models, the second on testing it on a more realistic scenario. The results indicate that the combination of Reinforcement Learning and Prospect Theory could be a descriptive account of decision- making in binary decision tasks. A two-state space configuration, together with a non- saturating reward function appears to be the best setup to capture behaviour in said task. Moreover, analysing the parameters of the models it becomes evident that payoff variability has an impact on speed of learning and randomness of choice. The same modelling approach fails to capture behaviour in a more complex task, indicating that more complex models might be needed to provide a computational account of decisions from experience in non-trivial tasks

Neural representations of social and non-social uncertainty in human decision making

The social landscape is filled with an intricate web of species-specific desired objects and course of actions. Humans are highly social animals and, as they navigate this landscape, they need to produce adapted decision-making behaviour. Traditionally social and non-social neural mechanisms affecting choice have been investigated using different approaches. Recently, in an effort to unite these findings, two main theories have been proposed to explain how the brain might encode social and non-social motivational decision-making: the extended common currency and the social valuation specific schema (Ruff & Fehr 2014). One way to test these theories is to directly compare neural activity related to social and non-social decision outcomes within the same experimental setting. Here we address this issue by focusing on the neural substrates of social and non-social forms of uncertainty. Using functional magnetic resonance imaging (fMRI) we directly compared the neural representations of reward and risk prediction and errors (RePE and RiPE) in social and non- social situations using gambling games. We used a trust betting game to vary uncertainty along a social dimension (trustworthiness), and a card game (Preuschoff et al. 2006) to vary uncertainty along a non-social dimension (pure risk). The trust game was designed to maintain the same structure of the card game. In a first study, we exposed a divide between subcortical and cortical regions when comparing the way these regions process social and non-social forms of uncertainty during outcome anticipation. Activity in subcortical regions reflected social and non-social RePE, while activity in cortical regions correlated with social RePE and non-social RiPE. The second study focused on outcome delivery and integrated the concept of RiPE in non-social settings with that of fairness and monetary utility maximisation in social settings. In particular these results corroborate recent models of anterior insula function (Singer et al. 2009; Seth 2013), and expose a possible neural mechanism that weights fairness and uncertainty but not monetary utility. The third study focused on functionally defined regions of the early visual cortex (V1) showing how activity in these areas, traditionally considered only visual, might reflect motivational prediction errors in addition to known perceptual prediction mechanisms (den Ouden et al 2012). On the whole, while our results do not support unilaterally one or the other theory modeling the underlying neural dynamics of social and non-social forms of decision making, they provide a working framework where both general mechanisms might coexist

Designing Activities for Collaboration at Classroom Scale Using Shared Technology

Although researchers, teachers and policy makers broadly agree on the benefits of collaborative learning, there appears to be less clarity regarding how effective collaboration can be realised at classroom scale. Research in Computer-Supported Collaborative Learning (CSCL), Human-Computer Interaction (HCI), simulation-based learning and related fields has produced a considerable range of applications that aim to support collaboration in classrooms. Grounded in well-established theories of how humans learn, many such applications have shown promising results within the context of small research studies. However, most of those research-driven applications never matured beyond the prototype stage and few are available today as products that schools can easily use and adopt. Many systems lack flexibility or require too much time, hardware, technical skills or other resources to be effectively implemented. Furthermore, teachers can be overwhelmed by managing large groups of students engaged in complex, computer-supported tasks. This thesis investigates how forms of whole-classroom activity can be supported by combining shareable technologies with simulation, team play and orchestration. New designs are explored to help large groups engage and discuss at multiple scales (from pairs and small groups to the entire classroom) in ways that effectively include each student and use the teacher's limited resources efficiently. Moreover, this research aims to devise and validate a conceptual framework that can guide future design, orchestration and evaluation of such activities. Three in-situ studies were conducted to address these goals. The first study involved the design of a climate change simulation to support a professional training course. Iterative design and video analysis resulted in the formulation of the Collaborative Learning Orchestration for Verbal Engagement and Reflection (CLOVER) framework. This framework comprises a suite of conceptual tools and recommendations that aim to help designers and teachers create, orchestrate and evaluate decision-based simulations for whole-classroom use. Two follow-up studies were conducted to validate the usability and usefulness of CLOVER. One of them aimed to replicate the previous findings in a similar context and resulted in the design of a sustainable, whole-classroom simulation for students to discuss finance decisions. The other used CLOVER to expand an existing desktop application (a~language comprehension task for children) to classroom scale. In sum, the three studies provide substantial empirical evidence, suggesting that CLOVER-based applications can effectively reconcile learning needs (collaboration) and technological affordances (shareable devices) with the inherent benefits and constraints of teacher-driven, co-located environments. Furthermore, the findings contribute to a better understanding of what it means to design for sustainability in this context

The Social Epistemology of Experimental Economics

Ana Cristina Cordeiro dos Santos was born in Lisbon, Portugal, in 1971. She received her B.Sc. degree in Economics from Technical University of Lisbon, in Portugal, in 1994, and a MA degree in Social Policy from Roskilde University, in Denmark, in 1995. Since 1996 she has been a teaching assistant at Instituto Superior de Ciências do Trabalho e da Empresa (ISCTE), in Lisbon. She obtained a MPhil degree in Philosophy of Economics at the Erasmus Institute for Philosophy of Economics, Erasmus University Rotterdam, in 2001. She completed her Ph.D. in Philosophy of Economics at the same institute.This thesis analysed the experimental process of knowledge production. It investigated how scientists build their confidence in knowledge generated by a process in which both the means and the outcomes of knowledge production are re-constructed. The study of experimental practice in the natural and human sciences supports the view that scientists are convinced that they have produced the phenomenon of interest when they achieve a three-way coherence between the three components of the experimental system: the experimental procedure, the instrumental model and the phenomenal model. When the three-way coherence is achieved, experimenters believe that they have created an experimental system that succeeded in producing the phenomenon of interest. The relation of coherence among the three components of the experimental system justifies belief in the experimental results because the threeway alignment supports each one of them and thus the experimental result conveyed by the phenomenal model. This was the underlying principle of the argument from coherence that justifies the way by which experimenters form belief in experimental results. However, it was also noted that the three-way alignment is not sufficient to justify belief in experimentally generated knowledge. Two additional arguments were presented that reinforced the epistemic value of the three-way coherence. The argument from materiality asserts that the direct engagement of the subject matter in knowledge production (both in the natural and human domains) renders experimental results and the coherences supporting them non-trivial achievements. The coherent problem-solutions arrived at carry knowledge about the subject under scrutiny because scientists cannot fully control it to meet their prior expectations. However, the argument from materiality does not satisfactorily account for experimenters’ confidence in experimental results. The participation of the subject matter might still be severely constrained by the problem-situation at hand or by the plasticity of the experimental systems. The argument from sociality asserts that the social dimension of knowledge production encourages the generation of fruitful problem-situations and reliable problem-solutions by bringing to the production process a vast number of resources of practice. The three arguments in conjunction lead to a broader conclusion: the greater the number and the greater the heterogeneity of the resources (material, conceptual and social) involved in knowledge production, the higher the epistemic status of the relations of coherence established given that they are the result of practices that have explored relevant courses of action to the resolution of interesting problem-situations

Linguistic Politeness Beyond Modernity. A Critical Reconsideration of Politeness Theories

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Multi-Agent Learning for Security and Sustainability

This thesis studies the application of multi-agent learning in complex domains where safety and sustainability are crucial. We target some of the main obstacles in the deployment of multi-agent learning techniques in such domains. These obstacles consist of modelling complex environments with multi-agent interaction, designing robust learning processes and modelling adversarial agents. The main goal of using modern multi-agent learning methods is to improve the effectiveness of behaviour in such domains, and hence increase sustainability and security. This thesis investigates three complex real-world domains: space debris removal, critical domains with risky states and spatial security domains such as illegal rhino poaching. We first tackle the challenge of modelling a complex multi-agent environment. The focus is on the space debris removal problem, which poses a major threat to the sustainability of earth orbit. We develop a high-fidelity space debris simulator that allows us to simulate the future evolution of the space debris environment. Using the data from the simulator we propose a surrogate model, which enables fast evaluation of different strategies chosen by the space actors. We then analyse the dynamics of strategic decision making among multiple space actors, comparing different models of agent interaction: static vs. dynamic and centralised vs. decentralised. The outcome of our work can help future decision makers to design debris removal strategies, and consequently mitigate the threat of space debris. Next, we study how we can design a robust learning process in critical domains with risky states, where destabilisation of local components can lead to severe impact on the whole network. We propose a novel robust operator κ which can be combined with reinforcement learning methods, leading to learning safe policies, mitigating the threat of external attack, or failure in the system. Finally, we investigate the challenge of learning an effective behaviour while facing adversarial attackers in spatial security domains such as illegal rhino poaching. We assume that such attackers can be occasionally observed. Our approach consists of combining Bayesian inference with temporal difference learning, in order to build a model of the attacker behaviour. Our method can effectively use the partial observability of the attacker’s location and approximate the performance of a full observability case. This thesis therefore presents novel methods and tackles several important obstacles in deploying multi-agent learning algorithms in the real-world, which further narrows the reality gap between theoretical models and real-world applications

Applications of robust optimal control to decision making in the presence of uncertainty

This thesis is concerned with robustness of decision making in financial economics. Feedback control models developed in engineering are applied to three separate though linked problems in order to examine the role and impact of robustness in the creation and application of decision rules. Three problems are examined using robust optimal control techniques to evaluate the impact of robustness and stability in financial economic models. The first problem examines the use of linear models of robust optimal control in the pricing of castastrophe based derivatives and finds its relative performance to be superior to the popular jump diffusion and stochastic volatility models in the pricing of these emerging instruments. The novelty of the approach arises from the examination of the impact of robustness and stability of the pricing solution. The second problem involves robustness and stability of hedging. An alternative method of creating hedging rules is developed. The method is based on robust control Lyapunov functions that are simple, robust and stable in operation, yet in practice are not so conservative that they eliminate all trading gains. The third problem involves the development of robust control policies for managing risk, using non-linear robust optimal control techniques to provide clear evidence of superior performance of robust models when compared with existing VAR and EVT approaches to risk management. The novelty in the approach arises from the development of a simple and powerful risk management metric