Bounded rationality

Bounded rationality is the idea that rationality is limited when individuals make decisions: by the tractability of the decision problem, the cognitive limitations of the mind, and the time available to make the decision. Decision-makers, in this view, act as satisficers, seeking a satisfactory solution rather than an optimal one.

Herbert A. Simon proposed bounded rationality as an alternative basis for the mathematical modeling of decision-making, as used in economics, political science and related disciplines. It complements "rationality as optimization", which views decision-making as a fully rational process of finding an optimal choice given the information available.[1] Simon used the analogy of a pair of scissors, where one blade represents "cognitive limitations" of actual humans and the other the "structures of the environment", illustrating how minds compensate for limited resources by exploiting known structural regularity in the environment.[1] Many economics models assume that people are on average rational, and can in large enough quantities be approximated to act according to their preferences. The concept of bounded rationality revises this assumption to account for the fact that perfectly rational decisions are often not feasible in practice because of the intractability of natural decision problems and the finite computational resources available for making them.

Some models of human behavior in the social sciences assume that humans can be reasonably approximated or described as "rational" entities, as in rational choice theory or Downs Political Agency Models.[2]


The term was coined by Herbert A. Simon. In Models of Man, Simon points out that most people are only partly rational, and are irrational in the remaining part of their actions. In another work, he states "boundedly rational agents experience limits in formulating and solving complex problems and in processing (receiving, storing, retrieving, transmitting) information".[3] Simon describes a number of dimensions along which "classical" models of rationality can be made somewhat more realistic, while sticking within the vein of fairly rigorous formalization. These include:

  • limiting the types of utility functions
  • recognizing the costs of gathering and processing information
  • the possibility of having a "vector" or "multi-valued" utility function

Simon suggests that economic agents use heuristics to make decisions rather than a strict rigid rule of optimization. They do this because of the complexity of the situation, and their inability to process and compute the expected utility of every alternative action. Deliberation costs might be high and there are often other concurrent economic activities also requiring decisions.

Model extensions

As decision-makers have to make decisions about how and when to decide, Ariel Rubinstein proposed to model bounded rationality by explicitly specifying decision-making procedures.[4] This puts the study of decision procedures on the research agenda.

Gerd Gigerenzer opines that decision theorists have not really adhered to Simon's original ideas. Rather, they have considered how decisions may be crippled by limitations to rationality, or have modeled how people might cope with their inability to optimize. Gigerenzer proposes and shows that simple heuristics often lead to better decisions than theoretically optimal procedures.[2]

Huw Dixon later argues that it may not be necessary to analyze in detail the process of reasoning underlying bounded rationality.[5] If we believe that agents will choose an action that gets them "close" to the optimum, then we can use the notion of epsilon-optimization, which means we choose our actions so that the payoff is within epsilon of the optimum. If we define the optimum (best possible) payoff as , then the set of epsilon-optimizing options S(ε) can be defined as all those options s such that:


The notion of strict rationality is then a special case (ε=0). The advantage of this approach is that it avoids having to specify in detail the process of reasoning, but rather simply assumes that whatever the process is, it is good enough to get near to the optimum.

From a computational point of view, decision procedures can be encoded in algorithms and heuristics. Edward Tsang argues that the effective rationality of an agent is determined by its computational intelligence. Everything else being equal, an agent that has better algorithms and heuristics could make "more rational" (more optimal) decisions than one that has poorer heuristics and algorithms.[6] Tshilidzi Marwala and Evan Hurwitz in their study on bounded rationality observed that advances in technology (e.g. computer processing power because of Moore's law, artificial intelligence and big data analytics) expand the bounds that define the feasible rationality space. Because of this expansion of the bounds of rationality, machine automated decision making makes markets more efficient.[7]

Relationship to behavioral economics

Bounded rationality implies the idea that humans take reasoning shortcuts that may lead to suboptimal decision-making. Behavioral economists engage in mapping the decision shortcuts that agents use in order to help increase the effectiveness of human decision-making. One treatment of this idea comes from Cass Sunstein and Richard Thaler's Nudge.[8][9] Sunstein and Thaler recommend that choice architectures are modified in light of human agents' bounded rationality. A widely cited proposal from Sunstein and Thaler urges that healthier food be placed at sight level in order to increase the likelihood that a person will opt for that choice instead of less healthy option. Some critics of Nudge have lodged attacks that modifying choice architectures will lead to people becoming worse decision-makers.[10][11]

Influence on social network structure

Recent research has shown that bounded rationality of individuals may influence the topology of the social networks that evolve among them. In particular, Kasthurirathna and Piraveenan[12] have shown that in socio-ecological systems, the drive towards improved rationality on average might be an evolutionary reason for the emergence of scale-free properties. They did this by simulating a number of strategic games on an initially random network with distributed bounded rationality, then re-wiring the network so that the network on average converged towards Nash equilibria, despite the bounded rationality of nodes. They observed that this re-wiring process results in scale-free networks. Since scale-free networks are ubiquitous in social systems, the link between bounded rationality distributions and social structure is an important one in explaining social phenomena.

See also


  1. ^ a b Gigerenzer, Gerd; Selten, Reinhard (2002). Bounded Rationality: The Adaptive Toolbox. MIT Press. ISBN 978-0-262-57164-7.
  2. ^ a b Mancur Olson, Jr. ([1965] 1971). The Logic of Collective Action: Public Goods and the Theory of Groups, 2nd ed. Harvard University Press, Description, Table of Contents, and preview.
  3. ^ Oliver E. Williamson, p. 553, citing Simon.
  4. ^ Rubinstein, Ariel (1997). Modeling bounded rationality. MIT Press. ISBN 9780262681001.
  5. ^ Moss; Rae, eds. (1992). "Some Thoughts on Artificial Intelligence and Economic Theory". Artificial Intelligence and Economic Analysis. Edward Elgar. pp. 131–154. ISBN 978-1852786854.
  6. ^ Tsang, E.P.K. (2008). "Computational intelligence determines effective rationality". International Journal on Automation and Control. 5 (1): 63–6. doi:10.1007/s11633-008-0063-6.
  7. ^ Marwala, Tshilidzi; Hurwitz, Evan (2017). Artificial Intelligence and Economic Theory: Skynet in the Market. London: Springer. ISBN 978-3-319-66104-9.
  8. ^ Thaler, Richard H., Sunstein, Cass R. (April 8, 2008). Nudge: Improving Decisions about Health, Wealth, and Happiness. Yale University Press. ISBN 978-0-14-311526-7. OCLC 791403664.CS1 maint: Uses authors parameter (link)
  9. ^ Thaler, Richard H., Sunstein, Cass R. and Balz, John P. (April 2, 2010). "Choice Architecture". doi:10.2139/ssrn.1583509. SSRN 1583509.CS1 maint: Uses authors parameter (link)
  10. ^ Wright, Joshua; Ginsberg, Douglas (February 16, 2012). "Free to Err?: Behavioral Law and Economics and its Implications for Liberty". Library of Law & Liberty.
  11. ^ Sunstein, Cass (2009-05-13). Going to extreems: How Like Minds Unite and Divide. ISBN 9780199793143.
  12. ^ Kasthurirathna, Dharshana; Piraveenan, Mahendra. (2015). "Emergence of scale-free characteristics in socioecological systems with bounded rationality". Scientific Reports. 7.

Further reading

  • Bayer, R. C., Renner, E., & Sausgruber, R. (2009). Confusion and reinforcement learning in experimental public goods games. NRN working papers 2009-22, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
  • Elster, Jon (1983). Sour Grapes: Studies in the Subversion of Rationality. Cambridge, UK: Cambridge University Press. ISBN 978-0-521-25230-0.
  • Gigerenzer, Gerd & Selten, Reinhard (2002). Bounded Rationality. Cambridge: MIT Press. ISBN 978-0-262-57164-7.
  • Hayek, F.A (1948) Individualism and Economic order
  • Kahneman, Daniel (2003). "Maps of bounded rationality: psychology for behavioral economics" (PDF). The American Economic Review. 93 (5): 1449–75. CiteSeerX doi:10.1257/000282803322655392.
  • March, James G. (1994). A Primer on Decision Making: How Decisions Happen. New York: The Free Press. ISBN 978-0-02-920035-3.
  • Simon, Herbert (1957). "A Behavioral Model of Rational Choice", in Models of Man, Social and Rational: Mathematical Essays on Rational Human Behavior in a Social Setting. New York: Wiley.
  • March, James G. & Simon, Herbert (1958). Organizations. John Wiley and Sons. ISBN 978-0-471-56793-6.
  • Simon, Herbert (1990). "A mechanism for social selection and successful altruism". Science. 250 (4988): 1665–8. doi:10.1126/science.2270480. PMID 2270480.
  • Simon, Herbert (1991). "Bounded Rationality and Organizational Learning". Organization Science. 2 (1): 125–134. doi:10.1287/orsc.2.1.125.
  • Tisdell, Clem (1996). Bounded Rationality and Economic Evolution: A Contribution to Decision Making, Economics, and Management. Cheltenham, UK: Brookfield. ISBN 978-1-85898-352-3.
  • Williamson, Oliver E. (1981). "The economics of organization: the transaction cost approach". American Journal of Sociology. 87 (3): 548–577 (press +). doi:10.1086/227496.

External links

Agent-based computational economics

Agent-based computational economics (ACE) is the area of computational economics that studies economic processes, including whole economies, as dynamic systems of interacting agents. As such, it falls in the paradigm of complex adaptive systems. In corresponding agent-based models, the "agents" are "computational objects modeled as interacting according to rules" over space and time, not real people. The rules are formulated to model behavior and social interactions based on incentives and information. Such rules could also be the result of optimization, realized through use of AI methods (such as Q-learning and other reinforcement learning techniques).The theoretical assumption of mathematical optimization by agents in equilibrium is replaced by the less restrictive postulate of agents with bounded rationality adapting to market forces. ACE models apply numerical methods of analysis to computer-based simulations of complex dynamic problems for which more conventional methods, such as theorem formulation, may not find ready use. Starting from initial conditions specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other, including learning from interactions. In these respects, ACE has been characterized as a bottom-up culture-dish approach to the study of economic systems.ACE has a similarity to, and overlap with, game theory as an agent-based method for modeling social interactions. But practitioners have also noted differences from standard methods, for example in ACE events modeled being driven solely by initial conditions, whether or not equilibria exist or are computationally tractable, and in the modeling facilitation of agent autonomy and learning.The method has benefited from continuing improvements in modeling techniques of computer science and increased computer capabilities. The ultimate scientific objective of the method is to "test theoretical findings against real-world data in ways that permit empirically supported theories to cumulate over time, with each researcher’s work building appropriately on the work that has gone before." The subject has been applied to research areas like asset pricing, competition and collaboration, transaction costs, market structure and industrial organization and dynamics, welfare economics, and mechanism design, information and uncertainty, macroeconomics, and Marxist economics.

Ariel Rubinstein

Ariel Rubinstein (Hebrew: אריאל רובינשטיין) (born April 13, 1951) is an Israeli economist who works in Economic Theory, Game Theory and Bounded Rationality.

Bounded emotionality

Bounded emotionality is a communications studies approach to dealing with emotional control in the workplace. Emotional control simply refers to how employers and employees handle the range of emotions that naturally occur in the workplace. These emotions can occur because of work, or they can be brought into work from an employee's home life. Bounded emotionality was proposed by Dennis K. Mumby and Linda L. Putnam. Mumby and Putnam (1992) stress that bounded emotionality encourages the expression of a wide range of emotions. Their theory encourages expression of emotions because it is a way to maintain interpersonal relationships and boundaries among people in the organization. Additionally, the expression of emotions strengthens work relations because people bond over mutual feelings. Bounded emotionality is a broad framework for organizations to use when dealing with emotions. It has six defining characteristics. The characteristics are: intersubjective limitations, spontaneously emergent work feelings, tolerance of ambiguity, heterarchy of values, integrated self identity and authenticity, and community.

Computational economics

Computational economics is a research discipline at the interface of computer science, economics, and management science. This subject encompasses computational modeling of economic systems, whether agent-based, general-equilibrium, macroeconomic, or rational-expectations, computational econometrics and statistics, computational finance, computational tools for the design

of automated internet markets, programming tools specifically designed for computational economics, and pedagogical tools for the teaching of computational economics. Some of these areas are unique to computational economics, while others extend traditional areas of economics by solving problems that are difficult to study without the use of computers and associated numerical methods.Computational economics uses computer-based economic modeling for the solution of analytically and statistically formulated economic problems. A research program, to that end, is agent-based computational economics (ACE), the computational study of economic processes, including whole economies, as dynamic systems of interacting agents. As such, it is an economic adaptation of the complex adaptive systems paradigm. Here the "agent" refers to "computational objects modeled as interacting according to rules," not real people. Agents can represent social, biological, and/or physical entities. The theoretical assumption of mathematical optimization by agents in equilibrium is replaced by the less restrictive postulate of agents with bounded rationality adapting to market forces, including game-theoretical contexts. Starting from initial conditions determined by the modeler, an ACE model develops forward through time driven solely by agent interactions. The ultimate scientific objective of the method is "to ... test theoretical findings against real-world data in ways that permit empirically supported theories to cumulate over time, with each researcher’s work building appropriately on the work that has gone before."Computational solution tools include for example software for carrying out various matrix operations (e.g. matrix inversion) and for solving systems of linear and nonlinear equations. For a repository of public-domain computational solution tools, visit here.

The following journals specialize in computational economics: ACM Transactions on Economics and Computation, Computational Economics, Journal of Applied Econometrics, Journal of Economic Dynamics and Control, and the Journal of Economic Interaction and Coordination.


Controversy is a state of prolonged public dispute or debate, usually concerning a matter of conflicting opinion or point of view. The word was coined from the Latin controversia, as a composite of controversus – "turned in an opposite direction," from contra – "against" – and vertere – to turn, or versus (see verse), hence, "to turn against."

Daniel Goldstein

Daniel G. Goldstein (born 1969) is an American cognitive psychologist known for the specification and testing of heuristics and models of bounded rationality in the field of judgment and decision making. He is an honorary research fellow at London Business School and works with Microsoft Research as a principal researcher.

Economics of scientific knowledge

The economics of scientific knowledge is an approach to understanding science which is predicated on the need to understand scientific knowledge creation and dissemination in economic terms.The approach has been developed as a contrast to the sociology of scientific knowledge, which places scientists in their social context and examines their behavior using social theory. The economics of scientific knowledge typically involves thinking of scientists as having economic interests with these being thought of as utility maximisation and science as being a market process. Modelling strategies might use any of a variety of approaches including the neoclassical, game theoretic, behavioural (bounded rationality) information theoretic and transaction costs. Boumans and Davis (2010) mention Dasgupta and David (1994) as being an interesting early example of work in this area.

Ernst Fehr

Ernst Fehr (born June 21, 1956 in Hard, Austria) is an Austrian-Swiss behavioral economist and neuroeconomist and a Professor of Microeconomics and Experimental Economic Research, as well as the vice chairman of the Department of Economics at the University of Zürich, Switzerland. His research covers the areas of the evolution of human cooperation and sociality, in particular fairness, reciprocity and bounded rationality.

He is also well known for his important contributions to the new field of neuroeconomics, as well as to behavioral economics, behavioral finance and experimental economics. According to IDEAS/REPEC, he is the second-most influential German-speaking economist, and is ranked at 86th globally.In 2010 Ernst Fehr founded, together with his brother, Gerhard Fehr, FehrAdvice & Partners, the first globally operating consultancy firm completely dedicated to behavioral economics.

In 2016, Fehr was ranked as the most influential economist in Germany, Austria, and Switzerland.


FORR (FOr the Right Reasons) is a cognitive architecture for learning and problem solving inspired by Herbert A. Simon's ideas of bounded rationality and satisficing. It was first developed in the early 1990s at the City University of New York. It has been used in game playing, robot pathfinding, recreational park design, spoken dialog systems, and solving NP-hard constraint satisfaction problems, and is general enough for many problem solving applications.

Gerd Gigerenzer

Gerd Gigerenzer (born September 3, 1947, Wallersdorf, Germany) is a German psychologist who has studied the use of bounded rationality and heuristics in decision making. Gigerenzer is director emeritus of the Center for Adaptive Behavior and Cognition (ABC) at the Max Planck Institute for Human Development and director of the Harding Center for Risk Literacy, both in Berlin, Germany.

Gigerenzer investigates how humans make inferences about their world with limited time and knowledge. He proposes that, in an uncertain world, probability theory is not sufficient; people also use smart heuristics, that is, rules of thumb. He conceptualizes rational decisions in terms of the adaptive toolbox (the repertoire of heuristics an individual or institution has) and the ability to choose a good heuristics for the task at hand. A heuristic is called ecologically rational to the degree that it is adapted to the structure of an environment.

Gigerenzer argues that heuristics are not irrational or always second-best to optimization, as the accuracy-effort trade-off view assumes, in which heuristics are seen as short-cuts that trade less effort for less accuracy. In contrast, his and associated researchers' studies have identified situations in which "less is more", that is, where heuristics make more accurate decisions with less effort. This contradicts the traditional view that more information is always better or at least can never hurt if it is free. Less-is-more effects have been shown experimentally, analytically, and by computer simulations.

Herbert A. Simon

Herbert Alexander Simon (June 15, 1916 – February 9, 2001) was an American economist, political scientist and cognitive psychologist, whose primary research interest was decision-making within organizations and is best known for the theories of "bounded rationality" and "satisficing". He received the Nobel Prize in Economics in 1978 and the Turing Award in 1975. His research was noted for its interdisciplinary nature and spanned across the fields of cognitive science, computer science, public administration, management, and political science. He was at Carnegie Mellon University for most of his career, from 1949 to 2001.Notably, Simon was among the pioneers of several modern-day scientific domains such as artificial intelligence, information processing, decision-making, problem-solving, organization theory, and complex systems. He was among the earliest to analyze the architecture of complexity and to propose a preferential attachment mechanism to explain power law distributions.

List of game theorists

This is a list of notable economists, mathematicians, political scientists, and computer scientists whose work has added substantially to the field of game theory. For a list of people in the field of video games rather than game theory, please see list of ludologists.

Derek Abbott - quantum game theory and Parrondo's games

Susanne Albers - algorithmic game theory and algorithm analysis

Kenneth Arrow - voting theory (Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel 1972)

Robert Aumann - equilibrium theory (Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel 2005)

Robert Axelrod - repeated Prisoner's Dilemma

Tamer Başar - dynamic game theory and application robust control of systems with uncertainty

Cristina Bicchieri - epistemology of game theory

Olga Bondareva - Bondareva–Shapley theorem

Steven Brams - cake cutting, fair division, theory of moves

Jennifer Tour Chayes - algorithmic game theory and auction algorithms

John Horton Conway - combinatorial game theory

William Hamilton - evolutionary biology

John Harsanyi - equilibrium theory (Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel 1994)

Monika Henzinger - algorithmic game theory and information retrieval

Naira Hovakimyan - differential games and adaptive control

Peter L. Hurd - evolution of aggressive behavior

Rufus Isaacs - differential games

Anna Karlin - algorithmic game theory and online algorithms

Michael Kearns - algorithmic game theory and computational social science

Sarit Kraus - non-monotonic reasoning

John Maynard Smith - evolutionary biology

Oskar Morgenstern - social organization

John Forbes Nash - Nash equilibrium (Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel 1994)

John von Neumann - Minimax theorem, expected utility, social organization, arms race

J. M. R. Parrondo - games with a reversal of fortune, such as Parrondo's games

Charles E. M. Pearce - games applied to queuing theory

George R. Price - theoretical and evolutionary biology

Anatol Rapoport - Mathematical psychologist, early proponent of tit-for-tat in repeated Prisoner's Dilemma

Julia Robinson - proved that fictitious play dynamics converges to the mixed strategy Nash equilibrium in two-player zero-sum games

Alvin E. Roth - market design (Nobel Memorial Prize in Economic Sciences 2012)

Ariel Rubinstein - bargaining theory, learning and language

Thomas Jerome Schaefer - computational complexity of perfect-information games

Suzanne Scotchmer - patent law incentive models

Reinhard Selten - bounded rationality (Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel 1994)

Claude Shannon - studied cryptography and chess; sometimes called "the father of information theory"

Lloyd Shapley - Shapley value and core concept in coalition games (Nobel Memorial Prize in Economic Sciences 2012)

Thomas Schelling - bargaining (Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel 2005) and models of segregation

Myrna Wooders - coalition theory

Menu cost

In economics, a menu cost is the cost to a firm resulting from changing its prices. The name stems from the cost of restaurants literally printing new menus, but economists use it to refer to the costs of changing nominal prices in general. In this broader definition, menu costs might include updating computer systems, re-tagging items, and hiring consultants to develop new pricing strategies as well as the literal costs of printing menus. More generally, the menu cost can be thought of as resulting from costs of information, decision and implementation resulting in bounded rationality. Because of this expense, firms sometimes do not always change their prices with every change in supply and demand, leading to nominal rigidity.

Generally, the effect on the firm of small shifts in price (by changes in supply and/or demand, or else because of slight adjustments in monetary policy) is relatively minor compared to the costs of notifying the public of this new information. Therefore, the firm would rather exist in slight disequilibrium than incur the menu costs.

Quantal response equilibrium

Quantal response equilibrium (QRE) is a solution concept in game theory. First introduced by Richard McKelvey and Thomas Palfrey,

it provides an equilibrium notion with bounded rationality. QRE is not an equilibrium refinement, and it can give significantly different results from Nash equilibrium. QRE is only defined for games with discrete strategies, although there are continuous-strategy analogues.

In a quantal response equilibrium, players are assumed to make errors in choosing which pure strategy to play. The probability of any particular strategy being chosen is positively related to the payoff from that strategy. In other words, very costly errors are unlikely.

The equilibrium arises from the realization of beliefs. A player's payoffs are computed based on beliefs about other players' probability distribution over strategies. In equilibrium, a player's beliefs are correct.

Reinhard Selten

Reinhard Justus Reginald Selten (5 October 1930 – 23 August 2016) was a German economist, who won the 1994 Nobel Memorial Prize in Economic Sciences (shared with John Harsanyi and John Nash). He is also well known for his work in bounded rationality and can be considered as one of the founding fathers of experimental economics.

Search cost

Search costs are one facet of transaction costs or switching costs. Rational consumers will continue to search for a better product or service until the marginal cost of searching exceeds the marginal benefit. Search theory is a branch of microeconomics that studies decisions of this type.

The costs of searching are divided into external and internal costs (Smith et al. 1999). External costs include the monetary costs of acquiring the information, and the opportunity cost of the time taken up in searching. External costs are not under the consumer's control, and all he or she can do is choose whether or not to incur them. Internal costs include the mental effort given over to undertaking the search, sorting the incoming information, and integrating it with what the consumer already knows. Internal costs are determined by the consumer's ability to undertake the search, and this in turn depends on intelligence, prior knowledge, education and training. These internal costs are the background to the study of bounded rationality.

The Internet was expected to eliminate search costs (Pereira 2005). For example, electronic commerce was predicted to cause disintermediation as search costs become low enough for end-consumers to incur them directly instead of employing retailers to do this for them. This would in turn lead to lower prices and less variation between prices quoted by different sellers.

Social heuristics

Social heuristics as a tool of bounded rationality are thought to guide behavior and decisions in the social environment. Social environments tend to be characterised by complexity and uncertainty, and agents with limited informational or cognitive resources may rely on simple rules of thumb to make decisions. The class of phenomena described by social heuristics overlap with those typically investigated by social psychology and game theory. At the intersection of these fields, social heuristics have been applied to explain cooperation in the prisoner's dilemma, based on the argument that cooperation is typically advantageous in daily life, and therefore people develop a cooperation heuristic that gets applied even to one-shot anonymous interactions (the so-called "social heuristics hypothesis" of human cooperation).

Within social psychology, some researchers have viewed heuristics as closely linked to cognitive biases. Others have argued that these biases result from the application of social heuristics depending on the structure of the environment that they operate in. Researchers in the latter approach treat the study of social heuristics as closely linked to social rationality, a field of research that applies the ideas of bounded rationality and heuristics to the realm of social environments. According to them, social heuristics include those that may use social information, operate in social contexts, or both. For instance, the follow-the-majority heuristic uses social information as inputs but is not necessarily applied in a social context, while the equity-heuristic uses non-social information but in a social context such as the allocation of parental resources amongst offspring. Such heuristics may be used by humans and other animals, but may also be potentially applied to artificial intelligent systems.

Utility maximization problem

For a less technical introduction, see Utility.In microeconomics, the utility maximization problem is the problem consumers face: "how should I spend my money in order to maximize my utility?" It is a type of optimal decision problem. It consists of choosing how much of each available good or service to consume, taking into account a constraint on total spending as well as the prices of the goods.

Topics in game theory
of games
See also
Institutional economists
Key concepts and ideas
Related fields

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