Building Persuasive Robots with Social Power Strategies

Can social power endow social robots with the capacity to persuade? This paper represents our recent endeavor to design persuasive social robots. We have designed and run three different user studies to investigate the effectiveness of different bases of social power (inspired by French and Raven's theory) on peoples' compliance to the requests of social robots. The results show that robotic persuaders that exert social power (specifically from expert, reward, and coercion bases) demonstrate increased ability to influence humans. The first study provides a positive answer and shows that under the same circumstances, people with different personalities prefer robots using a specific social power base. In addition, social rewards can be useful in persuading individuals. The second study suggests that by employing social power, social robots are capable of persuading people objectively to select a less desirable choice among others. Finally, the third study shows that the effect of power on persuasion does not decay over time and might strengthen under specific circumstances. Moreover, exerting stronger social power does not necessarily lead to higher persuasion. Overall, we argue that the results of these studies are relevant for designing human--robot-interaction scenarios especially the ones aiming at behavioral change

Cooperation in Online Conversations: The Response Times as a Window Into the Cognition of Language Processing

Measuring the cognitive cost of interpreting the meaning of sentences in a conversation is a complex task, but it is also at the core of Sperber and Wilson's Relevance Theory. In cognitive sciences, the delay between a stimulus and its response is often used as an approximation of the cognitive cost. We have noticed that such a tool had not yet been used to measure the cognitive cost of interpreting the meaning of sentences in a free-flowing and interactive conversation. The following experiment tests the ability to discriminate between sentences with a high cognitive cost and sentences with a low cognitive cost using the response time of the participants during an online conversation in a protocol inspired by the Turing Test. We have used violations of Grice's Cooperative Principle to create conditions in which sentences with a high cognitive cost would be produced. We hypothesized that response times are directly correlated to the cognitive cost required to generate implicatures from a statement. Our results are coherent with the literature in the field and shed some new light on the effect of violations on the humanness of a conversational agent. We show that violations of the maxim of Relation had a particularly important impact on response times and the perceived humanness of a conversation partner. Violations of the first maxim of Quantity and the fourth maxim of Manner had a lesser impact, and only on male participants

An analysis of pay and occupational differences by gender and race in Brazil - 1987 to 2006

This thesis investigates the magnitude and evolution of gender and racial occupational segregation and wage gaps in Brazil from 1987 to 2006. First, we provide the construction of a new harmonized and temporally consistent re-classification of the occupational codes using the Brazilian household survey, the PNADs. This new occupational classification permits an examination of the evolution of the Brazilian occupational structure over a protracted period of time. Second, we examine the occupational structure in Brazil assessing both the extent and trends in gender and racial based occupational segregation. We use several wellknown indices of segregation (Duncan and Duncan, 1955; Moir and Selby-Smith, 1979; Karmel and Maclachlan, 1988; Silber, 1989) and focus on the evolution over time of the occupational segregation across formal and non-formal labour markets. An attempt is made to assess the main forces driving changes in occupational segregation over time by employing a decomposition of the segregation measures developed by Deutsch, Flueckiger and Silber (2009). Third, we investigate the magnitude and evolution of gender and racial pay gaps in Brazil by employing several decomposition techniques. Together with the standard Oaxaca-Blinder decomposition, we apply the Brown, Moon and Zoloth (1980) decomposition technique, which allows us to account for the impact of occupational segregation on the wage gap. We explore the impact of the selection process on our decomposition results by employing different parametric corrections (the Heckman (1979) and Lee (1983) corrections). Several sensitivity checks are also implemented and alternative correction methods investigated such as the non-parametric imputation method by Olivetti and Petrongolo (2008) and the local wage gap estimation by Machado (2011). Fourth, we attempt to provide a comprehensive portrait of gender and racial wage gaps across the entire wage distribution while exploring the impact of gender and racial occupational segregation on wage determination in the Brazilian labour market. Our analysis particularly focuses on the evolution of the impact of female and non-white occupational intensity on wage outcomes and disparities. We employ quantile regression analysis in order to investigate the role of female and non-white occupational intensity at different points along the conditional wage distribution. We then apply two different decomposition techniques, proposed by Machado and Mata (2005) and Melly (2006), and by Firpo, Fortin and Lemieux (2009), to investigate the determinants of wage disparities at these different points in the wage distribution and to understand how these determinants vary across the wage distribution. Finally, we offer some concluding remarks, discuss the limitation of the research and provide an agenda for future research on the themes investigated in this thesis

Determining the effect of human cognitive biases in social robots for human-robotm interactions

The research presented in this thesis describes a model for aiding human-robot interactions based on the principle of showing behaviours which are created based on 'human' cognitive biases by a robot in human-robot interactions. The aim of this work is to study how cognitive biases can affect human-robot interactions in the long term. Currently, most human-robot interactions are based on a set of well-ordered and structured rules, which repeat regardless of the person or social situation. This trend tends to provide an unrealistic interaction, which can make difficult for humans to relate ‘naturally’ with the social robot after a number of relations. The main focus of these interactions is that the social robot shows a very structured set of behaviours and, as such, acts unnaturally and mechanical in terms of social interactions. On the other hand, fallible behaviours (e.g. forgetfulness, inability to understand other’ emotions, bragging, blaming others) are common behaviours in humans and can be seen in regular social interactions. Some of these fallible behaviours are caused by the various cognitive biases. Researchers studied and developed various humanlike skills (e.g. personality, emotions expressions, traits) in social robots to make their behaviours more humanlike, and as a result, social robots can perform various humanlike actions, such as walking, talking, gazing or emotional expression. But common human behaviours such as forgetfulness, inability to understand other emotions, bragging or blaming are not present in the current social robots; such behaviours which exist and influence people have not been explored in social robots. The study presented in this thesis developed five cognitive biases in three different robots in four separate experiments to understand the influences of such cognitive biases in human–robot interactions. The results show that participants initially liked to interact with the robot with cognitive biased behaviours more than the robot without such behaviours. In my first two experiments, the robots (e.g., ERWIN, MyKeepon) interacted with the participants using a single bias (i.e., misattribution and empathy gap) cognitive biases accordingly, and participants enjoyed the interactions using such bias effects: for example, forgetfulness, source confusions, always showing exaggerated happiness or sadness and so on in the robots. In my later experiments, participants interacted with the robot (e.g., MARC) three times, with a time interval between two interactions, and results show that the likeness the interactions where the robot shows biased behaviours decreases less than the interactions where the robot did not show any biased behaviours. In the current thesis, I describe the investigations of these traits of forgetfulness, the inability to understand others’ emotions, and bragging and blaming behaviours, which are influenced by cognitive biases, and I also analyse people’s responses to robots displaying such biased behaviours in human–robot interactions

Cross-cultural evidence for the influence of positive self-evaluation on cross-cultural differences in well-being

Poster Session F - Well-Being: abstract F197We propose that cultural norms about realism and hedonism contribute to the cross-cultural differences in well-being over and above differences in objective living conditions. To test this hypothesis, we used samples from China and the United States. Results supported the mediating role of positive evaluative bias in cross-cultural differences in well-being.postprin

Values and need satisfaction across 20 world regions

Poster Session F - Motivation/Goals: abstract F78Intrinsic valuing predicts the satisfaction of psychological needs (Niemiec, Ryan, & Deci, 2009). We conceptually replicate and extend this finding across 20 world regions. In multi-level models, Schwartz’s (1992) self-transcendence value was positively related to autonomy, competence, and relatedness satisfaction, even when controlling for the Big Five.postprin