Two studies were conducted to compare the efficacy of two behavioral prompts in increasing safety-belt use. Two approaches were used in both studies. The first approach involved showing a sign with the message, "Please Buckle Up, I Care" to unbuckled drivers leaving a parking lot; the second involved displaying the nationwide slogan "Click it or Ticket" (CioT). Participants were 1,822 unbuckled drivers exiting two student parking lots of a large university. Research assistants identified an unbuckled driver, flashed one of two signs, and recorded whether the driver buckled after the prompt, as well as the driver's facial reactions and hand gestures. Of the unbuckled drivers, 34% buckled following the Flash-for-Life (FfL) prompt, and 26% with the CioT prompt (p < .05). Drivers gave significantly more positive facial expressions and hand gestures following FfL vs. CioT prompt (p < .05). The second study used a variation of the procedures in Study I and implemented the intervention at a large department store. Driver age was measured with the previous driver variables. Participants were 1,990 unbuckled drivers exiting a parking lot. Results showed no significant differences in terms of compliance to signs (20.8% for FfL and 20.4% for CioT, p's > .05). Significant differences in hand gestures, facial expressions and ages were detected as a function of compliance, regardless of sign (all p's < .001). Specifically, drivers who complied with either prompt were more likely to be younger, and present positive hand gestures and facial expressions. Implications and future directions are discussed. / Master of Science

The purpose of the present investigation was to assess the relationships among the contingency of teachers' reactions as perceived by the pupils, the pupils' academic performance, and internality. One might expect that children who perceive their teacher as contingently rewarding and punishing would achieve higher grades and test scores than those who view their teachers' reactions as unrelated to their behavior. It is believed that children's perceptions of the contingencies of their teacher's responses may be more highly related to achievement behavior than the teacher's actual distribution of rewards and punishments. Perceived contingency of punishments and rewards may be important determiners of achievement. The perception of punishments as noncontingent is likely to be negatively related to achievement; however, the same may not be true of noncontingent reward. Indiscriminate dispersion of rewards could have a motivating effect or, as one might infer from Paris & Cairns (1972), no effect at all. Internality and achievement are expected to be positively related, perhaps more so for boys than for girls, as the trend of previous evidence suggests. Because of conflicting reports, no firm expectation can be formulated with regard to sex differences and the effectiveness of IAR+ versus IAR- scores.

This new economic era is characterized by organizations managing a diverse workforce across different generations. These generations have different work values, goals, and expectations on their employer, which poses various challenges for human resource managers in managing and retaining employees from different generations. The most recent generation that is currently entering the workforce is generation Y, and they are described as the younger generation with high employee turnover and reluctance to pledge long-term work commitments. Therefore, it is in the interest of organizations to tailor reward schemes that resonate with generation Yers in fostering organizational commitment. Thus, this thesis aims to examine how extrinsic and intrinsic rewards affect organizational commitment in generation Y. To answer these questions, this thesis reviews previous literature and use the self-determination theory to see how the different rewards affect the organizational commitment of generation Y. This study has been conducted through interviews with employees that are born within the age spectrum of generation Y (1980-1999) to understand how reward systems affect organizational commitment in generation Y employees. This thesis found that intrinsic rewards affect organizational commitment in a far more positive manner than extrinsic rewards. Furthermore, intrinsic rewards resonate to a greater extent with the employees from generation Y and should, therefore, be a priority for organizations. Moreover, this thesis concludes that intrinsic rewards can more easily satisfy the basic psychological needs, which are, as according to the self-determination theory, crucial to foster organizational commitment.

Thesis (M. Ed.)--University of Hong Kong, 1995. / Includes bibliographical references (leaf 120-123).

Thesis (M. Ed.)--University of Hong Kong, 1991. / Includes bibliographical references (leaf 86-91).

Thesis (M.Ed.)--University of Hong Kong, 1991. / Includes bibliographical references (leaf 86-91). Also available in print.

Thesis (M.Ed.)--University of Hong Kong, 1995. / Includes bibliographical references (leaves 120-123). Also available in print.

Esta dissertação de mestrado apresenta um estudo sobre os efeitos de diferentes funções de recompensa, aplicadas em aprendizado por reforço multiagente, para o problema de roteamento de veículos, em redes de tráfego. São abordadas duas funções de recompensas que diferem no alinhamento do sinal numérico enviado do ambiente ao agente. A primeira função, chamada função individual, é alinhada à utilidade individual do agente (veículo ou motorista) e busca minimizar seu tempo de viagem. Já a segunda função, por sua vez, é a chamada difference rewards, essa é alinhada à utilidade global do sistema e tem por objetivo minimizar o tempo médio de viagem na rede (tempo médio de viagem de todos os motoristas). Ambas as abordagens são aplicadas em dois cenários de roteamento de veículos que diferem em: quantidade de motoristas aprendendo, topologia e, consequentemente, nível de complexidade. As abordagens são comparadas com três técnicas de alocação de tráfego presentes na literatura. Resultados apontam que os métodos baseados em aprendizado por reforço apresentam desempenho superior aos métodos de alocação de rotas. Além disso, o alinhamento da função de recompensa à utilidade global proporciona uma melhora significativa nos resultados quando comparados com a função individual. Porém, para o cenário com maior quantidade de agentes aprendendo simultaneamente, ambas as abordagens apresentam soluções equivalentes. / This dissertation presents a study on the effects of different reward functions applyed to multiagent reinforcement learning, for the vehicles routing problem, in traffic networks. Two reward functions that differ in the alignment of the numerical signal sent from the environment to the agent are addressed. The first function, called individual function is aligned with the agent’s (vehicle or driver) utility and seeks to minimize their travel time. The second function, is called difference rewards and is aligned to the system’s utility and aims to minimize the average travel time on the network (average travel time of all drivers). Both approaches are applied to two routing vehicles’ problems, which differ in the number of learning drivers, network topology and therefore, level of complexity. These approaches are compared with three traffic assignment techniques from the literature. Results show that reinforcement learning-based methods yield superior results than traffic assignment methods. Furthermore, the reward function alignment to the global utility, provides a significant improvement in results when compared with the individual function. However, for scenarios with many agents learning simultaneously, both approaches yield equivalent solutions.

Esta dissertação de mestrado apresenta um estudo sobre os efeitos de diferentes funções de recompensa, aplicadas em aprendizado por reforço multiagente, para o problema de roteamento de veículos, em redes de tráfego. São abordadas duas funções de recompensas que diferem no alinhamento do sinal numérico enviado do ambiente ao agente. A primeira função, chamada função individual, é alinhada à utilidade individual do agente (veículo ou motorista) e busca minimizar seu tempo de viagem. Já a segunda função, por sua vez, é a chamada difference rewards, essa é alinhada à utilidade global do sistema e tem por objetivo minimizar o tempo médio de viagem na rede (tempo médio de viagem de todos os motoristas). Ambas as abordagens são aplicadas em dois cenários de roteamento de veículos que diferem em: quantidade de motoristas aprendendo, topologia e, consequentemente, nível de complexidade. As abordagens são comparadas com três técnicas de alocação de tráfego presentes na literatura. Resultados apontam que os métodos baseados em aprendizado por reforço apresentam desempenho superior aos métodos de alocação de rotas. Além disso, o alinhamento da função de recompensa à utilidade global proporciona uma melhora significativa nos resultados quando comparados com a função individual. Porém, para o cenário com maior quantidade de agentes aprendendo simultaneamente, ambas as abordagens apresentam soluções equivalentes. / This dissertation presents a study on the effects of different reward functions applyed to multiagent reinforcement learning, for the vehicles routing problem, in traffic networks. Two reward functions that differ in the alignment of the numerical signal sent from the environment to the agent are addressed. The first function, called individual function is aligned with the agent’s (vehicle or driver) utility and seeks to minimize their travel time. The second function, is called difference rewards and is aligned to the system’s utility and aims to minimize the average travel time on the network (average travel time of all drivers). Both approaches are applied to two routing vehicles’ problems, which differ in the number of learning drivers, network topology and therefore, level of complexity. These approaches are compared with three traffic assignment techniques from the literature. Results show that reinforcement learning-based methods yield superior results than traffic assignment methods. Furthermore, the reward function alignment to the global utility, provides a significant improvement in results when compared with the individual function. However, for scenarios with many agents learning simultaneously, both approaches yield equivalent solutions.

Esta dissertação de mestrado apresenta um estudo sobre os efeitos de diferentes funções de recompensa, aplicadas em aprendizado por reforço multiagente, para o problema de roteamento de veículos, em redes de tráfego. São abordadas duas funções de recompensas que diferem no alinhamento do sinal numérico enviado do ambiente ao agente. A primeira função, chamada função individual, é alinhada à utilidade individual do agente (veículo ou motorista) e busca minimizar seu tempo de viagem. Já a segunda função, por sua vez, é a chamada difference rewards, essa é alinhada à utilidade global do sistema e tem por objetivo minimizar o tempo médio de viagem na rede (tempo médio de viagem de todos os motoristas). Ambas as abordagens são aplicadas em dois cenários de roteamento de veículos que diferem em: quantidade de motoristas aprendendo, topologia e, consequentemente, nível de complexidade. As abordagens são comparadas com três técnicas de alocação de tráfego presentes na literatura. Resultados apontam que os métodos baseados em aprendizado por reforço apresentam desempenho superior aos métodos de alocação de rotas. Além disso, o alinhamento da função de recompensa à utilidade global proporciona uma melhora significativa nos resultados quando comparados com a função individual. Porém, para o cenário com maior quantidade de agentes aprendendo simultaneamente, ambas as abordagens apresentam soluções equivalentes. / This dissertation presents a study on the effects of different reward functions applyed to multiagent reinforcement learning, for the vehicles routing problem, in traffic networks. Two reward functions that differ in the alignment of the numerical signal sent from the environment to the agent are addressed. The first function, called individual function is aligned with the agent’s (vehicle or driver) utility and seeks to minimize their travel time. The second function, is called difference rewards and is aligned to the system’s utility and aims to minimize the average travel time on the network (average travel time of all drivers). Both approaches are applied to two routing vehicles’ problems, which differ in the number of learning drivers, network topology and therefore, level of complexity. These approaches are compared with three traffic assignment techniques from the literature. Results show that reinforcement learning-based methods yield superior results than traffic assignment methods. Furthermore, the reward function alignment to the global utility, provides a significant improvement in results when compared with the individual function. However, for scenarios with many agents learning simultaneously, both approaches yield equivalent solutions.