Influencers On The Decision For Bariatric Surgery: A Pilot Study And Proposed Model

The costs associated with obesity in the United States are estimated to be $120 billion annually and an estimated 400,000 deaths. In addition, obesity has been shown to significantly diminish the quality of life through lower self-esteem, depression, and discomfort in social situations. Although the treatment for obesity, defined as a body mass index (BMI) of 40, through bariatric surgery is not new, the literature is lacking in explaining the decision to undergo the procedure. The research reported here employs a small sample as a pilot study to examine potential influences on the decision-making process. Scales from the existing literature, e.g., vanity, fear of success, are adapted for this research. The findings suggest that prospective patients seek an enhanced quality of life but criticism by loved ones does not significantly influence the decision. The results suggest several issues that should be examined in greater detail with a larger sample

Comparing The Machiavellianism Of Todays Indonesian College Students With U. S. College Students Of Today And The 1960s

The tactics and strategies that were suggested by Niccolo Machiavelli in The Prince (1513) have become synonymous with manipulative and unethical practices. Machiavellis writing to the politician has been used to describe business leaders as well. The business literature indicates that Machiavellian tactics do not guarantee success. The research we report examined the Machiavellian tendencies of college students in Indonesia and compare those results to the literature including the original U.S. student sample of the 1960s and the Harmon and Webster student sample published in 2002

The Identification of Competitive Structure Based on Situation and Situationally Characterized Respondents

Kevin L. Hammond is an assistant professor in the Department of Management and Marketing at the University of Tennessee at Martin. C. Richard Huston is an associate professor in the Department of Management and Marketing at Louisiana Tech University. Harry A. Harmon is an assistant professor in the Department of Marketing and Legal Studies at Central Missouri State University

Advantage Updating Applied to a Differential Game

An application of reinforcement learning to a linear-quadratic, differential game is presented. The reinforcement learning system uses a recently developed algorithm, the residual gradient form of advantage updating. The game is a Markov Decision Process (MDP) with continuous time, states, and actions, linear dynamics, and a quadratic cost function. The game consists of two players, a missile and a plane; the missile pursues the plane and the plane evades the missile. The reinforcement learning algorithm for optimal control is modified for differential games in order to find the minimax Presented at the Neural Information Processing Systems Conference, Denver, Colorado, November 28 - December 3, 1994. point, rather than the maximum. Simulation results are compared to the optimal solution, demonstrating that the simulated reinforcement learning system converges to the optimal answer. The performance of both the residual gradient and non-residual gradient forms of advantage updating an..

October 1995: Accepted for publication in Adaptive Behavior Reinforcement Learning Applied to a Differential Game

An application of reinforcement learning to a linear-quadratic, differential game is presented. The reinforcement learning system uses a recently developed algorithm, the residual-gradient form of advantage updating. The game is a Markov decision process with continuous time, states, and actions, linear dynamics, and a quadratic cost function. The game consists of two players, a missile and a plane; the missile pursues the plane and the plane evades the missile. Although a missile and plane scenario was the chosen test-bed, the reinforcement learning approach presented here is equally applicable to biologically based systems, such as a predator pursuing prey. The reinforcement learning algorithm for optimal control is modified for differential games to find the minimax point rather than the maximum. Simulation results are compared to the analytical solution, demonstrating that the simulated reinforcement learning system converges to the optimal answer. The performance of both the residual-gradient and non-residual-gradient forms of advantage updating and Q-learning are compared, demonstrating that advantage updating converges faster than Q-learning in all simulations. Advantage updating is also demonstrated to converge regardless of the time step duration; Q-learning is unable to converge as the time step duration grows small. Key words: reinforcement learning; advantage updating; dynamic programming; differential game