Organizational Psychology Revisited: Recent Applications of Goal-Setting Theory

Purpose:Goal Setting Theory(GST)created by Edwin Locke and Gary Latham has proven to be an incredibly versatile theory.Its widespread utilization has proven it to be a valuable theory to further explore and understand.The purpose of this paper is to examine current approaches to and practices of GST.Methodology:This systematic literature review is based on 12 recent articles using GST and examining their collective findings.The articles were a mix of theory description,quantitative experiments,empirical experiments,and literature review.Findings:Most of the reviewed literature agreed that further and more defined research would be greatly beneficial for future applications of this theory.Given the broad nature of this theory,a more defined approach would likely prove useful for future utilization.Value:Exploring the multitude of ways this theory has already been applied gives an understanding of shortcomings as well as successes.Reviewing the current available literature allows GST to be utilized in a more precise way in the future.

Game Interactive Learning:A New Paradigm towards Intelligent Decision-Making

Decision-making plays an essential role in various real-world systems like automatic driving,traffic dispatching,information system management,and emergency command and control.Recent breakthroughs in computer game scenarios using deep reinforcement learning for intelligent decision-making have paved decision-making intelligence as a burgeoning research direction.In complex practical systems,however,factors like coupled distracting features,long-term interact links,and adversarial environments and opponents,make decision-making in practical applications challenging in modeling,computing,and explaining.This work proposes game interactive learning,a novel paradigm as a new approach towards intelligent decision-making in complex and adversarial environments.This novel paradigm highlights the function and role of a human in the process of intelligent decision-making in complex systems.It formalizes a new learning paradigm for exchanging information and knowledge between humans and the machine system.The proposed paradigm first inherits methods in game theory to model the agents and their preferences in the complex decision-making process.It then optimizes the learning objectives from equilibrium analysis using reformed machine learning algorithms to compute and pursue promising decision results for practice.Human interactions are involved when the learning process needs guidance from additional knowledge and instructions,or the human wants to understand the learning machine better.We perform preliminary experimental verification of the proposed paradigm on two challenging decision-making tasks in tactical-level War-game scenarios.Experimental results demonstrate the effectiveness of the proposed learning paradigm.