This paper proposes a new method to combine choice- and text data to infer moral motivations from people’s actions. To do this, we rely on moral rhetoric, in other words, extracting moral values from verbal expressions with Natural Language Processing techniques. We use moral rhetoric based on a well-established moral, psychological theory called Moral Foundations Theory. We use moral rhetoric as input in Discrete Choice Models to gain insights into moral behaviour based on people’s words and actions. We test our method in a case study of voting and party defection in the European Parliament. Our results indicate that moral rhetoric have significant explanatory power in modelling voting behaviour. We interpret the results in the light of political science literature and propose ways for future investigations.@en

Social routing schemes are widely regarded as promising tools to reduce traffic congestion in urban networks. We contribute to the growing literature on such schemes and their effect on travel behavior, by exploring the interaction between the characteristics and framing of the scheme on the one hand, and travelers' moral personality and moral motivations on the other hand. Our method uses a two-wave stated intention experiment eliciting preferences in a hypothetical context where a social routing scheme is presumed to have been established already. This is followed by a morality survey. We hypothesize and then confirm the following: when a social routing scheme is framed and designed as an altruistic effort requesting personal sacrifices for the benefit of other travelers, people who strongly adhere to care related notions of morality are attracted to such a scheme. On the contrary, a scheme that is designed and framed as a collective endeavour which would also benefit participating travelers attracts those who strongly adhere to moral notions related to fairness. We derive tentative policy recommendations from our findings, suggesting that a collective good scheme, albeit more difficult to implement, is likely to be more viable in the long run.@en

We examine identifiability and distinguishability in Decision Field Theory (DFT) models and highlight pitfalls and how to avoid them. In the past literature, the models’ parameters have been put forward as being able to capture the psychological processes in a decision maker's mind during deliberation. DFT models have been widely used to analyse human decision making behaviour, and many empirical applications in the choice modelling domain rely solely on data concerning the observed final choice. This raises the question if such data are rich enough to allow for the identification of the model's parameters. Insight into identifiability and distinguishability is crucial as it allows the researcher to determine which behavioural and psychological conclusions can or cannot be drawn from the estimated DFT model and how a DFT model can be specified in such a way that resulting parameters have meaningful interpretations. In this paper, we address this issue. To do this, we first show which specifications of DFT are equivalent to conventional probit models. Then, building on this equivalence result, we apply established analytical methods to highlight and explain the identification and distinguishability issues that arise when estimating DFT models on conventional choice data. We find evidence that some of the DFT models’ special cases suffer from identifiability issues. Our results warrant caution when DFT models are used to infer psychological processes and human behaviour from conventional choice data, and they help researchers choose the correct specification of DFT models.@en

Discrete Choice Models are valuable tools for quantitative decision-making analysis: they allow analysts to draw behavioural conclusions from data, better understand and predict choices, and evaluate policies. However, up until recently, they had a blind spot for morality. Moral values often play an essential role in decision-making; fairness or loyalty can deter people from following self-interest. Moral motivations can also prompt decision-makers to change their minds when contemplating a dilemma or hide their preferences when they want to avoid judgement. These notions are not aligned with crucial behavioural assumptions traditional Discrete Choice Models are based on, such as stable preferences echoing through choices or decision-makers maximizing their utility. This thesis aims to develop and test new Discrete Choice Models that help identify morality in a mathematically rigorous framework, thus increasing the behavioural realism of Discrete Choice Models in moral decision-making. To do this, it uses two approaches. @en

Theories of decision-making are routinely based on the notion that decision-makers choose alternatives which align with their underlying preferences—and hence that their preferences can be inferred from their choices. In some situations, however, a decision-maker may wish to hide his or her preferences from an onlooker. This paper argues that such obfuscation-based choice behavior is likely to be relevant in various situations, such as political decision-making. This paper puts forward a simple and tractable discrete choice model of obfuscation-based choice behavior, by combining the well-known concepts of Bayesian inference and information entropy. After deriving the model and illustrating some key properties, the paper presents the results of an obfuscation game that was designed to explore whether decision-makers, when properly incentivized, would be able to obfuscate effectively, and which heuristics they employ to do so. Together, the analyses presented in this paper provide stepping stones towards a more profound understanding of obfuscation-based decision-making.

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