Behavioral economics and behavioral change policies: theoretical foundations and practical applications to promote well-being in the Italian context

The PhD thesis is divided in four parts: Part 1. The first part provides a historical and theoretical background of the most influential models of decision-making developed in the last few decades in the fields of economy and psychology. It begins with an overview of the basic principles of Neoclassical Economics and follows with a description of the studies that led to the birth and spread of Behavioral Economics. Part 2. The second part focuses on the main Behavioral Change policy programs developed in the last few years all over the world, particularly concentrating on the main features of the so called Nudge and on its alternative approach called Boost. Part 3. The center of attention of the third part are the researches carried out in the last few years in the Italian context by the author of this PhD thesis. The studies are divided in two main categories: the first includes experiments on the individual’s preferences within a laboratory setting while the second includes field experiments that study the actual choices of the individuals. Part 4. The last part offers a detailed description from a Behavioral Analytic point of view of some of the main concepts of Behavioral Economics and Nudge by highlight their common and divergent traits on a theoretical and practical level. This part is a an attempt to dig deeper into the topic of decisional processes and choices, by taking into account the extensive knowledge that derives from more than one century of studies on human behavior

Fast Strategies in Waiter-Client Games on KnK_n

Waiter-Client games are played on some hypergraph $(X,\mathcal{F})$, where $\mathcal{F}$ denotes the family of winning sets. For some bias $b$, during each round of such a game Waiter offers to Client $b+1$ elements of $X$, of which Client claims one for himself while the rest go to Waiter. Proceeding like this Waiter wins the game if she forces Client to claim all the elements of any winning set from $\mathcal{F}$. In this paper we study fast strategies for several Waiter-Client games played on the edge set of the complete graph, i.e. $X=E(K_n)$, in which the winning sets are perfect matchings, Hamilton cycles, pancyclic graphs, fixed spanning trees or factors of a given graph.Comment: 38 page

Learning to adapt in dialogue systems : data-driven models for personality recognition and generation.

Dialogue systems are artefacts that converse with human users in order to achieve some task. Each step of the dialogue requires understanding the user's input, deciding on what to reply, and generating an output utterance. Although there are many ways to express any given content, most dialogue systems do not take linguistic variation into account in both the understanding and generation phases, i.e. the user's linguistic style is typically ignored, and the style conveyed by the system is chosen once for all interactions at development time. We believe that modelling linguistic variation can greatly improve the interaction in dialogue systems, such as in intelligent tutoring systems, video games, or information retrieval systems, which all require specific linguistic styles. Previous work has shown that linguistic style affects many aspects of users' perceptions, even when the dialogue is task-oriented. Moreover, users attribute a consistent personality to machines, even when exposed to a limited set of cues, thus dialogue systems manifest personality whether designed into the system or not. Over the past few years, psychologists have identified the main dimensions of individual differences in human behaviour: the Big Five personality traits. We hypothesise that the Big Five provide a useful computational framework for modelling important aspects of linguistic variation. This thesis first explores the possibility of recognising the user's personality using data-driven models trained on essays and conversational data. We then test whether it is possible to generate language varying consistently along each personality dimension in the information presentation domain. We present PERSONAGE: a language generator modelling findings from psychological studies to project various personality traits. We use PERSONAGE to compare various generation paradigms: (1) rule-based generation, (2) overgenerate and select and (3) generation using parameter estimation models-a novel approach that learns to produce recognisable variation along meaningful stylistic dimensions without the computational cost incurred by overgeneration techniques. We also present the first human evaluation of a data-driven generation method that projects multiple stylistic dimensions simultaneously and on a continuous scale