The science of decision making is a relatively new field operating at the nexus of psychology, economics, and neuroscience. Combining emerging technologies in neuroscience to psychological and economical methodologies, Decision Science studies and analyzes how people make judgments and choices. Rather than dictate how decisions should be made, Decision Science seeks to understand how real world decisions actually are made.
Game theory, also known as interactive decision theory, was pioneered by Princeton mathematician John Von Neumann and has been utilized in studies such as tennis, chess, and childrearing. This theory studies the ways in which deliberate actions among agents (the rational decision makers) produce particular outcomes with respect to the preferences of those agents. Game theory may be used to explain, to predict, and to evaluate human behavior in contexts where the outcome of action depends on what several agents choose to do and where their choices depend on what others choose to do. This “science of strategy” attempts to determine mathematically and logically the actions that “players” should take to secure the best outcomes for themselves in a wide array of “games.” Psychologists typically refer to game theory as the theory of social situations, which is perhaps a more accurate description of what constitutes game theory. Although game theory is relevant to games such as poker or bridge, research in game theory focuses on the interactions between groups of people.
There are two primary branches of game theory: cooperative and noncooperative game theory. These branches differ in how each one formalizes interdependence among the players. In the non-cooperative theory, a game functions as a detailed model of all the moves available to the players. In contrast, the cooperative theory abstracts away from this level of detail, and describes only the outcomes that result when the players come together in different combinations. In zero-sum games, for example, the interests of the players conflict totally, so that one person’s gain always is another’s loss. More typical are games with the potential for either mutual gain (positive sum) or mutual harm (negative sum), as well as some conflict. Recent research has focused on games that are neither zero sum nor purely cooperative. In these games, the players choose their actions separately, but their links to others involve elements of both competition and cooperation.
Recently there has been renewed interest in game theory in several research disciplines, with its applications ranging from the modeling of evolution to the design of distributed protocols. In the artificial intelligence field, game theory is emerging as the predominant formalism for studying strategic and cooperative interaction in multi-agent systems. Classical work provides rich mathematical foundations and equilibrium concepts, and the rapidly emerging field of computational game theory addresses a wide variety of algorithmic issues.
The prisoner’s dilemma is a game of strategy in social science offering insight into the concepts which govern the balance between cooperation and competition in business, in politics, and in social situations. The theory behind prisoner’s dilemma was developed by RAND Corporation scientists Merrill Flood and Melvin Dresher and was formalized by Princeton mathematician Albert W. Tucker.
In the traditional version of the game, the police have arrested two suspects and are in the process of interrogating the suspects in separate rooms. Each suspect can either confess, thereby implicating the other, or remain silent. No matter the other suspect’s actions or statements, confessing will improve each one’s situation. If on suspect confesses, the other suspect had better confess as well in order to avoid the harsh sentencing for a recalcitrant holdout. If the other suspect keeps silent, then one can obtain the favorable treatment accorded a state’s witness by confessing. Thus, confession is the dominant strategy for each suspect. However, when both confess, the outcome is worse for both than when both keep silent.
The prisoners’ dilemma may be applied to the fields of economics and business. For example, if two businesses are selling similar products, each business must decide on a pricing strategy for maximum product. Both companies exploit their joint market power when they charge a high price; each makes a profit of (for example) ten million dollars per month. If one company sets a competitively lower price, it wins a high number of customers away from the rival. Suppose its profit rises to twelve million dollars, and that of the rival falls to seven million. If both companies set low prices, the profit of each is nine million dollars. This low-price strategy is likened to the prisoner’s confession, and the high-price akin to keeping silent. Cheating or exploiting joint market power is each firm’s dominant strategy, but the outcome when both “cheat” is worse for each company than that of both cooperating.
In addition to business strategy, arms races among superpowers and nations provide another important example of the dilemma. Both countries are better off when they cooperate and avoid an arms race. Yet the dominant strategy for each is to arm itself heavily.
Computing and Information Technology
The study and analysis of normative or prescriptive decision making includes game theory, prisoner’s dilemma, and utility theory. Another subset of decision science is concerned primarily with decision support. Given what is known about the rational decision making process and actual behavior, this concentration of decision science centers upon researching and providing ways to aid people in their decision making. Professionals such as computer scientists and information technologists attempt to provide effective methods and tools for supporting human decision makers. Decision Analysis, Operational Research, and Decision Support Systems are examples of effective methodologies.
Decision analysis is popularly known as “applied decision theory.” Decision analysis approaches a decision problem systematically by structuring and breaking it down into smaller and manageable subproblems. This process provides an opportunity to consider the possible decision alternatives, available information, uncertainties involved, and relevant preferences of the decision maker. It also attempts to formally represent these components and combine them in a form of decision models, which are used to assess, evaluate and analyze alternatives.
The aim of operational research or operations research is similar to decision analysis, as this method applies analytical tools and mathematical models for decision support. However, the emphasis in operational research is on mathematical modelling and finding optimal solutions of mathematically defined problems instead of assessing given alternatives and finding effectives ones, as in decision analysis. Typical applications of operational research are characterized by the need of decision makers to allocate limited resources, such as time, energy and money. Operational research techniques include linear and nonlinear programming, network optimization models, combinatorial optimization, multi-objective decision making, and Markov analysis. This type of method is most commonly utilized in government, business, engineering, economics, and the natural and social sciences.
Decision Support Systems
Decision support systems are interactive computer-based information systems intended to help decision makers utilize data and models in order to identify and provide solutions to problems as well as make decisions. In contrast with decision analysis and operational research, DSS focuses on providing information technology for decision makers in companies and organizations. The emphasis is on providing relevant information and presenting it in a suitable form so as to maximize the decision making process and tasks.
Decision support systems can aid decision makers in a variety of different ways. They can store data and provide means to search for relevant data items. Advanced systems or techniques include query languages and data warehouses. Data can be viewed and analyzed using pivot tables and other methods of on-line analytical processing (OLAP). DSS can provide computational and statistical models, for instance for trend analysis. With data mining algorithms, the decision maker is able to find interesting or necessary patterns in data. The results may be presented in reports and tables, as well as graphically using advanced visualization techniques
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