Call for Papers

6th International Conference on Decision Economics (DECON'24)

Assuming human rationality in decision-making: An understanding of the normative bedrock of [standard] economics*

Even if utilities look very unnumerical today, the history of the experience of the theory of heat may repeat itself, as it happened, though in different forms and ways, for the theory of light, colours, and radio waves.” [von Neumann and Morgenstern, 1944, p. 17].

The year 2024 marks eighty years since the publication of von Neumann and Morgenstern’s [1944] seminal work “Theory of Games and Economic Behavior”. Since its inception in 1944, this work has been highly influential not only for being the boldest and methodical attempt to address the subject upon which modern game theory is based, but also for representing the first systematic application of formalised game theory to the social sciences. In the work, among other things, the two authors introduce the concept of stable sets and separate cooperative games from non-cooperative ones. Nevertheless, when faced with the problem of solving variable-sum games, they adopt the line that the solution space of such games effectively reduces to a negotiated solution and thus can be solved as coalition games via the concept of stable sets. The discussion is supplemented by mathematics underpinned by selfish-maximising rationality and complete information assumptions, which align with the 'economistic' paradigm and the related axiomatisation of formal systems.

This brings us to the nub of the matter: By analysing games played by perfectly rational and fully informed decision-makers, especially in the fields of economic relations and social organisation, von Neumann and Morgenstern’s [1944] seminal work also suggests the possibility of applying game theory on a large scale to address problems inherent to economic strategies and—increasingly and more soundly—to the framework of organisational, social, political, military, and environmental issues. Hence the idea of including this mathematical theory in various research projects and degree course syllabi gives rise to fruitful contamination between ideas belonging not only to different disciplines but even to three disciplinary fields, such as the formal sciences, the social and cognitive sciences.

Therefore, in addition to being relevant to contemporary game theory, the diverse nature [and complexity] of that seminal work is generally understood to go well beyond the mathematics of rational decision-making by interacting individuals. Aside from providing a much deeper theoretical understanding of expected utility theory, this understanding expands the academic scope beyond that of mathematicians and encompasses multiple fields of inquiry, making it a more comprehensive body of knowledge. From this perspective, it is appropriate to look back on von Neumann and Morgenstern’s [1944] work and reflect critically and insightfully on what it meant, particularly its implications for the social and cognitive sciences, how it developed, and where we are today, recalling two of its key points here: The adoption of individual decisions as the starting point of economic research and the underlying [or rather overlying] role of human rationality in framing decision processes.

There appears to be wide recognition that, building on that work, advanced research in economics—and related areas—began to involve decision analysis, which soon shifted from parametric studies to strategic analysis. With the former, roughly speaking, the economic agent takes prices as given when making decisions, without any concern for the price-determining behaviour of other agents; with the latter, the agent takes into account the predictable reactions of others to her choices. This being the case, the assumptions and beliefs that shape the concept of rationality underlying economic behaviour allow the field of possible decisions to be narrowed down while constituting the strengths and weaknesses of the related theoretical framework. Yes, it is widely known that the book by von Neumann and Morgenstern [1944] is presented based upon an axiomatic structure comprised of postulates articulating the standard economic model of rational choice in decision-making, considering the preferences of each individual for any possible event, and assuming that the probabilities of the events are given. Once the axioms have been formulated—the main one being the postulate of rationality, understood as the lack of contradictions—the cornerstone of standard economic theory can be traced back to a set of logically necessary relations of mere deductive procedures that would characterise human behaviour.

Interestingly enough, the two authors’ seminal work, to which this Call refers, also allows their view of utility as an objectively measurable natural phenomenon to emerge. The work also assumes that a single probability measure is defined for all events while preferring the frequentist theory of probability to the subjective one. This view seems to align with the pre-Paretian marginalist methodological approach to such an extent that “[e]ven if utilities look very unnumerical today, the history of the experience of the theory of heat may repeat itself, as it happened, though in different forms and ways, for the theory of light, colours and radio waves.” [von Neumann and Morgenstern, 1944, p. 17, brackets added]. In any case, the two authors argue that probability and preference can be axiomatised together, focusing thereby on probabilistic risk instead of dealing with uncertainty. Savage [1954], afterwards, while synthesising the ideas of Ramsey [1931], de Finetti [1930, 1931, 1937] and von Neumann and Morgenstern [1944], introduces a novel analytical framework and conditions that are necessary and sufficient for the existence and joint uniqueness of utility and probability, and the characterisation of individual choice as expected utility-maximising behaviour [i.e., a subjective approach to probability in a model of expected utilities à la von Neumann and Morgenstern, 1944].

All in all, unlike the earlier marginalist tradition, von Neumann and Morgenstern’s unprecedented approach to decision-making concerns game theory and expected utility. This approach broadens the traditional economic focus on the problem of choice when dealing with scarce resources since each choice can lead to multiple outcomes, each with different probabilities. When making a decision, indeed, an economic [neoclassical] agent can calculate the expected utility of each available option by weighing the utility of each possible outcome by its probability. To analyse the expected utility, therefore, the two authors introduce a system of postulates that, in essence, correspond to the completeness, continuity, and transitivity of both preferences and the probabilities attributed to various choices. Furthermore, each preference relationship is considered independent from all other events; in other words, external effects are neglected. Thus, both utility and probability are considered measurable, that is, enumerable. The overarching set of axioms ensures that probability and utility—hence, expected utility—reflect the properties of mathematical expectations. Consequently, by assuming that economic agents have complete information, one can specify their choices [i.e., the system’s solutions] corresponding to rational behaviours or, better, to behaviours that [globally] maximise their expected utility. 

Upon closer inspection, von Neumann and Morgenstern’s analysis contains a controversial aspect related to the concept of rationality, which can be understood in a descriptive and normative manner. The paradoxes raised by Allais [1953] and Ellsberg [1961], as well as other authors, make us question the descriptive validity of this concept. However, it could be argued that even its normative framework may also increase scepticism and give rise to criticism. In this wake, Savage’s Foundations of Statistics [1954] represents a significant contribution that builds on and advances the work of von Neumann and Morgenstern [1944]. The Foundations are widely regarded as the basis of modern inferential statistics and determine a crucial shift in the conceptual underpinnings of expected utility theory. Much debate surrounds the so-called "sure-thing principle" axiom, which Savage used for his theory. According to this axiom, the choice between two options [i.e., alternatives] should not be affected by features that have the same value in both options. The Ellsberg [1961] paradox challenges this axiom by contradicting it. This contradiction has paved the way for a more comprehensive theory, providing valuable insights into the diverse aspects of probability, in which the probability measure need not be additive.

Because of the axiomatic focus on rationality, economic agents are allowed to derive the ordinal utility functions from their order of preferences. This way of proceeding has been dominating the marginalist approach to economic studies since Pareto’s works [1897, 1906, and 1911]. Rational behaviour is then assumed as the agent’s choice between alternative options. This choice maximises a specific objective function or, echoing von Neumann and Morgenstern [1944], the value of her expected utility. This would be true even if we claimed or aspired to broaden the field of economics to include the most diverse social phenomena [as advocated by Becker and Nashat, 1997], from micro to macro scales, real-world issues affecting everyday life: As posited by von Neumann and Morgenstern’s formal analysis, humans would be constantly faced with maximisation constraints characterised by a single-dimensional utility function which determines preferences for their actions. Yet still, numerous doubts and controversies have arisen from various research streams—and continue to arise—regarding this conceptualisation of rational [self-referential] behaviour and the standard generalisation that flows into the construction of the homo œconomicus. Among these streams, experimental and computational methodologies stand out, both conducted through various operational methods, deductively and inductively. Also noteworthy is the interdisciplinary research on the border between economics and neurobiology adopting the rejection of the postulate of perfect rationality as a point of departure. It might remain relatively open to attribute a normative significance to the standard theory supported by a sort of Olympian rationality [Simon, 1983] to be equipped with, against which agents’ actual behaviour might be viewed as a divergence from it, even if systematic and inherent to human nature.

It is important to note that upon closer examination of these research streams, it can be suggested that the paradoxes do not necessarily demonstrate the irrational nature of human behaviour. Rather, they reveal that economic agents operate within a more complex context and evolutionary pattern than those assumed by expected utility theory, particularly in two critical aspects. Firstly, agents work under conditions of uncertainty, which is not of a probabilistic kind but of Keynesian-type uncertainty. Precisely, this type of uncertainty corresponds to a limited knowledge of the situation at hand. Keynes believed that when it is possible to give a more or less precise evaluation of expected probability, it should be integrated with another element, the degree of confidence we can have in our probability estimate. This research line is characterised by bounded rationality and satisficing behaviour, as proposed by Simon [1947, 1955, 1959]. Secondly, agents are not driven by a single motivation but by an intricate set of beliefs, passions, and interests, as the Enlightenment tradition emphasises. These motivations may be contradictory, vary over time, and are socially conditioned by social norms and relations. This contrasts with the solipsism adopted by the marginalist tradition, which is consistent with its methodological individualism and the subsequent [neoclassical] hypotheses to which it gave rise.

The argument that economic agents’ behaviour does not comply with the precepts of such rationality, as represented in the expected utility paradigm, sets the groundwork for interdisciplinary research, especially between economics, constructive mathematics, and cognitive sciences, focused on the distinctive characteristics of human behaviour and the logic of discovery (Simon, 1973; Langley et al., 1987; Velupillai, 2005). Both classical and modern behavioural economics give rise to this kind of interdisciplinary work by identifying, among other things, stylised facts that characterise research into judgment, choices, and human decisions while using them to build interpretative models of constructive and computable analysis.

The controversies following the 1944 book by von Neumann and Morgenstern mainly revolve around the conceptualisation of the rationality assumption that underlies the axioms of expected utility theory: rationality understood with a descriptive rather than normative emphasis. Arguably, these controversies are sharper in the study of human decision-making than in any other field of inquiry. Not surprisingly, in all their variations and formulations, utility theories, notoriously, along with the [unrealistic] assumptions to which they give rise in standard economics modelling, presuppose that humans are concerned with both stocks and flows of knowledge systematically, a well-organised and stable structure of preferences, and an excellent ability to compute utility values for all possible paths of action that are available to them. Although a certain academic belief regarding research methodology maintains that scientific disciplines do not have the purpose of explaining the functioning of the real world, the same authors—von Neumann and Morgenstern and similarly also Savage—considered their axioms to be both an abstract but realistic explanation of human economic behaviour and a standard for the adoption of rational decisions. Arrow [1951] also supports this idea [for an accurate reconstruction of these controversies, see Heukelom, 2014]. As with other research milestones, the work "Theory of Games and Economic Behavior" continues today to arouse keen interest but also a healthy scepticism, raising numerous doubts and questions, which makes the 'game' of academic discourse much more enjoyable, allowing research to progress and embrace greater representativeness and greater reliability of the results. Therefore, the eightieth anniversary of that work represents an opportunity to reassess its significance to economics, mathematics, statistics, philosophy, finance, ethics, cognitive sciences, and other fields. In addition, it presents an opportunity to evaluate what remains of it over eight decades of intellectual development.

The above and some broader questions and issues about the intellectual forces operating in the development of scientific thought will be examined in the course of the Conference together with the founding topics of DECON. This year, too, the challenge is undoubtedly both theoretical and paradigmatic but also rigorously methodological, empirically, and experimentally grounded. It applies to all aspects of the interdisciplinary methodological approach that stems from the work of von Neumann and Morgenstern [1944]. This approach concerns several fields of science, starting with mathematics, statistics, economics, and social structures while spilling over into other research fields. Papers in the 2024 edition of Decision Economics are encouraged to support more interdisciplinary work accordingly.

References and Background Reading

  • Abdellaoui, M., Baillon, A., Placido, L., & Wakker, P.P. (2011). The rich domain of uncertainty: Source functions and their experimental implementation. American Economic Review, 101 (2): 695-723.
  • Allais, M. (1953). Le comportement de l’homme rationnel devant le risque: Critique des postulats et axiomes de l’école américaine. Econometrica, 21(4): 503-546.
  • Allais, M., & Hagen, O. (eds.) (1979). Expected utility hypotheses and the Allais paradox: Contemporary discussions of the decisions under uncertainty with Allais’ rejoinder. Dordrecht, NL: Reidel.
  • Arrow, K. J. (1951). Alternative approaches to the theory of choice in risk-taking situations. Econometrica, 19(4): 404-437.
  • Arrow, K.J. (1971). Essays in the theory of risk-bearing. Chicago: Markham.
  • Arrow, K.J. (1982). Risk perception in psychology and economics. Economic Inquiry, 20(1): 1-9.
  • Arthur, W.B. (1994). Inductive reasoning and bounded rationality. The American Economic Review, 84(2): 406-411.
  • Aumann, R. J. (1987). Game theory. In J. Eatwell, M. Milgate, & P. Newman (eds.), The new Palgrave dictionary of economics, pp. 460-482. London: Macmillan.
  • Becker, G.S., & Nashat, G. (1997). The economics of life: From baseball to affirmative action to immigration, how real-world issues affect our everyday life. New York: McGraw-Hill.
  • Bell, D.E. (1982). Regret in decision making under uncertainty. Operations Research, 30(5): 961-981.
  • Benacerraf, P., & Putnam, H. (eds.) (1983) [1964]. Philosophy of mathematics: Selected readings. Cambridge, MA: Cambridge University Press.
  • Berg, N., & Gigerenzer, G. (2010). As-If behavioral economics: Neoclassical economics in disguise? History of Economic Ideas, 18(1): 133-166.
  • Bernoulli, D. (1738). Specimen theoriae novae de mensura sortis. Commentarii academiae scientiarum imperiales petropolitanae, 5: 175-192. En. tr. by L. Sommer (1954), Econometrica,22: 23-36.
  • Binmore, K. (1987; 1988). Modeling rational players: parts i and if. Economics and Philosophy, 3: 4.
  • Binmore, K.G. (1992). Fun and games: A text on game theory. Lexington, MA: D.C. Heath.
  • Binmore, K. (2009). Rational decisions. The Gorman Lectures in Economics. Princeton, NJ: Princeton University Press.
  • Binmore, K., & Dasgupta, P. (eds.) (1987). The Economics of bargaining. Oxford, UK: Basil Blackwell.
  • Bohm, P. (1967). On the theory of "second best". The Review of Economic Studies, 34(3): 301-314.
  • Borel, E. (1921). La theorie du jeu et les equations integrales a noyau symetrique. Comptes Rendus de l’Academie des Sciences, 173: 1304-1308.
  • Boumans, M. (2005). How economists model the world into numbers. London and New York: Routledge.
  • Brouwer, L. (1999) [1913]. Intuitionism and formalism. Bulletin (New Series) of the American Mathematical Society, 37(1): 55-64.
  • Camerer, C.F. (2003). Behavioral game theory: Experiments in strategic interaction. Princeton, NJ: Princeton University Press.
  • Conlisk, J. (1996). Why bounded rationality? Journal of Economic Literature, 34(2): 669-700.
  • Cyert, R.W., & March, J.G. (1963). A behavioral theory of the firm. Englewood Cliffs, NJ: Prentice-Hall.
  • Davidson, D., Suppes, P., & Siegel, S. (1957). Decision-making: an experimental approach. Stanford, CA: Stanford University Press.
  • Davis, J.B. (2003). The theory of the individual in economics. London, New York: Routledge.
  • Debreu, G. (1991). The mathematization of economic theory. American Economic Review, 81(1): 1-7.
  • de Finetti, B. (1930). Fondamenti logici del ragionamento probabilistico. Bollettino dell’Unione Matematica Italiana, 9: 258-61.
  • de Finetti, B. (1931). Probabilismo: saggio critico sulla teoria delle probabilità e sul valore della scienza. Napoli: Perrella.
  • de Finetti, B. (1937). La prévision: ses lois logiques, ses sources subjectives. Annals de l’Institute Henri Poincaré, 7: 1-68. Trans. and ed. H. Kyburg, in H. Kyburg, & H. Smokler (1964), Studies in subjective probabilities. New York: John Wiley.
  • de Finetti, B. (1974). The value of studying subjective evaluations of probability. In C.-A. S. von Holstein (ed.), The concept of probability in psychological experiments, pp. 1-14. Dordrecht, NL: Reidel.
  • Edwards, W. (1954). The theory of decision making. Psychological Bulletin, 51(4): 380-417.
  • Edwards, W. (1961). Behavioral decision theory. Annual Review of Psychology, 12: 473-498.
  • Ellsberg, D. (1961). Risk, ambiguity, and the Savage axioms. The Quarterly Journal of Economics, 75(4): 643-669.
  • Farrell, J., & Rabin, M. (1996). Cheap talk. Journal of Economic Perspectives, 10(3): 103-118.
  • Fishburn, P.C. (1974). Lexicographic orders, utilities and decision rules: A survey. Management Science, 20(11): 1442-1471.
  • Fishburn, P.C. (1989). Retrospective on the utility theory of von Neumann and Morgenstern. Journal of Risk and Uncertainty, 2: 127-157.
  • Fishburn, P.C. (1991). Decision theory: The next 100 years? Economic Journal, 101(404): 27-32.
  • Gandy, R.O. (1982). Limitations to mathematical knowledge. In D. van Dalen, D. Lascar, & J. Smiley (eds.), Logic Colloquium ’80, pp. 129-146. Amsterdam, Netherlands: North-Holland.
  • Gigerenzer, G., & Selten, R. (2002). Bounded rationality: The adaptive toolbox. Cambridge, MA: MIT Press.
  • Gilboa, I. (1987). Expected utility with purely subjective non-additive probabilities. Journal of Mathematical Economics, 16(1): 65-88.
  • Gillies, D. (2001). Bayesianism and the fixity of the theoretical framework. In D. Corfield, & J. Williamson (eds.), Foundations of Bayesianism, pp. 363-379, Dordrecht: Kluwer Academic Publishers.
  • Giocoli, N. (2003). Modeling rational agents: From interwar economics to early modern game theory. Cheltenham, UK: Edward Elgar.
  • Giocoli, N. (2005). Modeling rational agents the consistency view of rationality and the changing image of neoclassical economics. Cahiers d’économie Politique / Papers in Political Economy, 49: 177-208.
  • Hands, D.W. (2015). Normative rational choice theory: Past, present, and future. Available at SSRN, URL = or
  • Heukelom, F. (2010). Measurement and decision making at the University of Michigan in the 1950s and 1960s. Journal of the History of Behavioral Sciences, 46(2), 189-207.
  • Heukelom, F. (2014). Behavioral economics: A history. Cambridge, UK: Cambridge University Press.
  • Hogarth, R.M. (1989). Ambiguity and competitive decision making: Some implications and tests. Annals of Operations Research, 19: 31-50.
  • Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2): 263-292.
  • Kahneman, D., & Tversky, A. (1981). The framing of decisions and the psychology of choice. Science, 211: 453-458.
  • Khalil, E.L. (1995). Has economics progressed? Rectilinear, historicist, universalist, and evolutionary historiographies. History of Political Economy, 27(1): 43-87.
  • Kirman, A., & Salmon, M. (eds.) (1995). Learning and rationality in economics, Cambridge, UK: Blackwell.
  • Kolmogorov, A. (1950). Foundations of the theory of probability. New York: Chelsea.
  • Langley, P., Simon, H.A., Bradshaw, G.L., & Zytkow, J.M. (1987). Scientific discovery. Cambridge, MA: MIT Press.
  • Leonard, R.J. (1995). From parlor games to social science: von Neumann, Morgenstern and the creation of game theory 1928-1944. Journal of Economic Literature, 33(2): 730-761.
  • Lipman, B.L. (1991). How to decide how to decide how to...: Modeling limited rationality. Econometrica, 59(4): 1105-1125.
  • Loomes, G., & Sugden, R. (1982). Regret theory: An alternative theory of rational choice under uncertainty. Economic Journal, 92(368): 805-824.
  • Luce, R.D., & Krantz, D.H. (1971). Conditional expected utility. Econometrica, 39(2): 253-271.
  • Machina, M.J. (2009). Risk, ambiguity, and the rank-dependence axioms. American Economic Review, 99(1): 385-392.
  • March, J.G. (1978). Bounded rationality, ambiguity, and the engineering of choice. The Bell Journal of Economics, (9)2: 587-608.
  • Marschak, J. (1946). Neumann’s and Morgenstern’s new approach to static economics. Journal of Political Economy, 54(2): 97-115.
  • Marschak, J. (1950). Rational behavior, uncertain prospects, and measurable utility. Econometrica, 18: 111-141.
  • Mirowski, P. (1992). What were von Neumann and Morgenstern trying to accomplish? History of Political Economy, 24(suppl.): 113-147.
  • Mirowski, P. (2002). Machine dreams: Economics becomes a cyborg science. Cambridge, MA: Cambridge University Press.
  • Mongin, P. (2019). The Allais paradox: What it became, what it really was, what it now suggests to us. Economics and Philosophy, 35(3): 423-459.
  • Montesano, A. (2005). La nozione di razionalità in economia. Rivista Italiana degli Economisti, 10(1): 23-42.
  • Nash, J.F. (1951). Non-cooperative games. Annals of Mathematics, 54: 289-295.
  • O’Rand, A.M. (1992). Mathematizing social science in the 1950s: The early development and diffusion of game theory. History of Political Economy, 24(suppl.): 177-204.
  • Pareto, V. (1897). The new theories of economics. Journal of Political Economy, 5(4): 485-502.
  • Pareto, V. (1906). Manuale di economia politica con una introduzione alla scienza sociale. Milano: Società Editrice Libraria (Manuel d’économie politique. Traduit sur l’édition italienne par Alfred Bonnet—revue par l’auteur—Paris: Giard et Brière, 1909).
  • Pareto, V. (1911). Économie mathématique. Encyclopédie des sciences mathématiques pures et appliquées, Tome 1, Vol. 4, Paris: Gauthier-Villars.
  • Pólya, G. (1945). How to solve it. Princeton, NJ: Princeton University Press.
  • Pólya, G. (1954). Patterns of plausible inference, Mathematics and Plausible Reasoning. Vol. 2, Princeton, NJ: Princeton University Press.
  • Punzo, L. (1991). The school of mathematical formalism and the Viennese circle of mathematical economists. Journal of the History of Economic Thought, 13(1): 1-18.
  • Ramsey, F.P. (1931). Truth and probability. In R. Braithwaite, & F. Plumpton (eds.), The foundations of mathematics and other logical essays. London: K. Paul, Trench, Truber and Co.
  • Rider, R.E. (1992). Operations research and game theory: Early connections. History of Political Economy, 24(suppl.): 225-239.
  • Roncaglia, A. (2005). The wealth of ideas. Cambridge, UK: Cambridge University Press.
  • Roncaglia, A. (2009). Keynes and probability: An assessment. European Journal of the History of Economic Thought, 16(3): 485-510.
  • Roncaglia, A. (2019). The age of fragmentation. Cambridge, UK: Cambridge University Press.
  • Ross, D. (1997). Game theory. The Stanford Encyclopedia of Philosophy (Spring 2024 Edition), Edward N. Zalta & Uri Nodelman (eds.), URL = <>.
  • Rubinstein, A. (1988). Similarity and decision-making under risk (is there a utility theory resolution to the Allais paradox?). Journal of Economic Theory, 46(1): 145-153.
  • Rubinstein, A. (1998). Modeling bounded rationality. Cambridge, MA: MIT Press.
  • Samuelson, P.A. (1948). Consumption theory in terms of revealed preference. Economica, 15(60): 243-253.
  • Samuelson, P.A. (1952). Probability, utility, and the independence axiom. Econometrica, 20(4): 670-678.
  • Samuelson, P.A. (1998). How foundations came to be. Journal of Economic Literature, 36(3): 1375-1386.
  • Sargent, T. (1993). Bounded rationality in macroeconomics. Oxford, UK: Claredon Press.
  • Savage, L.J. (1951). The theory of statistical decision. Journal of the American Statistical Association, 46: 55-67.
  • Savage, L.J. (1954). The foundation of statistics. New York: John Wiley & Sons.
  • Shackle, G. (1949). Expectation in economics. Cambridge, UK: Cambridge University Press.
  • Schumpeter, J.A. (1954). History of economic analysis. New York: Oxford University Press.
  • Selten, R. (1978). The chain-store paradox. Theory and Decision, 9: 127-159.
  • Sen, A. (1987). Rational behaviour. In J. Eatwell, M. Milgate, & P. Newman (eds.), The new Palgrave: A dictionary of economics, pp. 68- 76. London: Macmillan.
  • Sent, E.-M. (1998). The evolving rationality of rational expectations: An assessment of Thomas Sargent’s achievements. Cambridge, MA: Cambridge University Press.
  • Sent, E.-M. (2004a). The legacy of Herbert Simon in game theory. Journal of Economic Behavior and Organization, 53(3): 303-317.
  • Sent, E.-M. (2004b). Behavioral economics: How psychology made its (limited) way back into economics. History of Political Economy, 36(4): 735-760.
  • Shubik, M. (1982). Game theory in the social sciences: Concepts and solutions. Cambridge, MA: MIT Press.
  • Simon, H.A. (1947). Administrative behavior. New York: Macmillan.
  • Simon, H.A. (1955). A behavioural model of rational choice. The Quarterly Journal of Economics, 69(1): 99-118.
  • Simon, H. A. (1956). Rational choice and the structure of environments. Psychological Review, 63(2), 129-138.
  • Simon, H.A. (1959). Theories of decision-making in economics and behavioral science. The American Economic Review, 49(3): 253-283.
  • Simon, H. A. (1973). Does scientific discovery have a logic? Philosophy of Science, 40(4): 471-480.
  • Simon, H.A. (1976). From substantive to procedural rationality. In S.J. Latsis (ed.), Method and appraisal in economics, pp. 129-148. Cambridge, UK: Cambridge University Press.
  • Simon, H. A. (1978). Rationality as process and as product of thought. The American Economic Review, 68(2): 1-16.
  • Simon, H. A. (1983). Reason in human affairs. Oxford: Basil Blackwell.
  • Simon, H. A., & Kulkarni, D. (1988). The processes of scientific discovery: The strategy of experimentation. Cognitive Science, 12(2): 139-175.
  • Slovic, P., & Tversky, A. (1974). Who accepts Savage’s axiom? Behavioral Science, 19(6): 368-373.
  • Slutsky, E. (1915). Sulla teoria del bilancio del consumatore. Giornale degli Economisti e Rivista di Statistica, 51: 1-26. Translated as "On the theory of the budget of the consumer", in K.E. Boulding, & G.J. Stigler (eds.) (1953), Readings in price theory, pp. 26-56. London: Allen & Unwin.
  • Stahl, D. (2014). Heterogeneity of ambiguity preferences. Review of Economics and Statistics, 96: 609-617.
  • Starmer, C. (2000). Developments in non-expected utility theory: The hunt for a descriptive theory of choice under risk. Journal of Economic Literature, 38(2): 332-382.
  • Starmer, C. (2005). Normative notions in descriptive dialogues. Journal of Economic Methodology, 12: 277-289.
  • Sugden, R. (1991). Rational choice: A survey of contributions from economics and psychology. Economic Journal, 101(407): 751-785.
  • Taleb, N.N. (2010). The black swan: The impact of the highly improbable (2nd ed.). New York: Random House.
  • Tarski, A. (in collaboration with) Mostowski, A., & Robinson, R.M. (1953). Undecidable theories. Amsterdam: North-Holland Publishing.
  • Tversky, A. (1969). Intransitivity of preferences. Psychological Review, 76(1): 31-48.
  • Tversky, A. (1972). Choice by elimination. Journal of Mathematical Psychology, 9(4): 341-367.
  • Tversky, A. (1975). A critique of expected utility theory: Descriptive and normative considerations. Erkenntnis, 9(2): 163-173.
  • Tversky, A., & Kahneman, D. (1974). Judgement under uncertainty: Heuristics and biases. Science, 185(4157): 1124-1131.
  • Tversky, A., & Kahneman, D. (1986). Rational choice and the framing of decisions. The Journal of Business , 59(4): S251-S278;
  • Van Horn, R., Mirowski, P., & Stapleford, T. A. (2011). Building Chicago economics. Cambridge, MA: Cambridge University Press.
  • Velupillai, K.V. (1997). Expository notes on computability and complexity in (arithmetical) games. Journal of Economic Dynamics and Control, 21(6): 955-979.
  • Velupillai, K.V. (2005). The unreasonable ineffectiveness of mathematics in economics. Cambridge Journal of Economics, 29(6): 849-872.
  • Velupillai, K.V. (2009). Uncomputability and undecidability in economic theory. Applied Mathematics and Computation, 215(4): 1404-1416.
  • Velupillai, K. V. (2010). Computable foundations for economics. London: Routledge.
  • Velupillai, K. V. (2011). Towards an algorithmic revolution in economic theory. Journal of Economic Surveys, 25(3): 401-430.
  • von Neumann, J. (1928). Zur theorie der gesellschaftsspiele. Mathematische Annalen, 100: 295-320. En. tr. in R. D. Luce & A. W. Tucker (eds.), Contributions to the theory of games, IV (1959), pp. 13-42. Princeton, NJ: Princeton University Press.
  • von Neumann, J. (1945). A model of general economic equilibrium. The Review of Economic Studies, 13(1): 1-9.
  • von Neumann, J. (1960). The mathematician. In J. Newman (ed.), The world of mathematics, Vol. IV, London: Allen & Unwin.
  • von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior. Princeton, NJ: Princeton University Press; II ed., 1947; III ed., 1953.
  • Vriend, N.J. (1996). Rational behavior and economic theory. Journal of Economic Behavior and Organization, 29(2): 263-285.
  • Wald, A. (1950). Statistical decision functions. New York: John Wiley and Sons.
  • Weintraub, E.R., & Mirowski, P. (1994): The pure and the applied: Bourbakism comes to mathematical economics. Science in Context, 7(2): 245-272.
  • Weintraub, E. R. (2002). How economics became a mathematical science. Durham and London: Duke University Press.
  • Williamson, J. (2003). Bayesianism and language change. Journal of Logic, Language and Information, 12: 53-97.
  • Zermelo, E. (1913). Über eine anwendung der mengenlehre auf die theorie des schachspiels ("On the application of set theory to the theory of chessgames"). Proceedings Fifth International Congress of Mathematicians (Cambridge, 22-28 Aug. 1912), pp. 501-504, Cambridge, UK: Cambridge University Press.

* Given the topicality of the subject, several verbs included in this Call are intentionally conjugated in the historical present.


  • The notion of measurement in utility theory, psychology, mathematics, and other areas of research.
  • Generative models for human decision-making in behavioural economics and cognitive sciences.
  • As-if behavioural economics and the bicycle repair shop: Neoclassical economics in disguise.
  • Routinely violating Savage’s Sure Thing Principle, that is, violating the law of total probability.
  • Treating the decision making agent’s mental state as a quantum state in Hilbert space.
  • Fall between two stools: Perspective on contemporary game theory as a meta-theory.
  • Heuristics of discovery: Data-driven versus tool-driven in computable and experimental methods.
  • Brouwerian constructivism as the appropriate approach to mathematical modelling in economics.
  • Beyond the veil of theory: Normative vs. descriptive and optimising vs. non-optimising approaches.
  • Look before you leap into Savage’s small world vs. large worlds of scientific discovery and macroeconomic enterprise.
  • Choice under uncertainty, problems solved and unsolved: Should a rational agent maximise expected utility?
  • On the inadequacy of game theory for the solution of real-world collective action problems.
  • The architecture of economic theory: Towards an evolutionary, processive conception of rationality.
  • Ecological rationality vs. internal consistency: Growing adherence to irrational consistencies.
  • Matching between a heuristic and the information structure in a particular environment.
  • De gustibus non est explanandum: Preference formation and the role of social norms.
  • How rationality and risk aversion change the rationale for discounting and choice of discount rate.
  • Rational choice and the framing of decision structuring: When rationality fails.
  • Decisional procedures: individual preference rankings vs. social ranking of preferences.
  • The elegance of the hedgehog: Normativity, probability, and meta-vagueness.
  • Normative treatment of expected utility: A goal achieved or a still open challenge?
  • The emergence of bluff in poker-like games and real life: Economics-related computing and decision-making.
  • Game theory: Recent breakthroughs in AI for multi-agent systems and their applications.
  • Interplays between economics, mathematics, and computer science: Studying heuristically complex societies.
  • Organisational behaviour and human decision processes: The human-problem solving research.
  • Autonomous agents and multi-agent systems: Multi-agent deep reinforcement learning.
  • Talk is cheap: How private information is shared through market and other mechanisms.
  • Decision-making in uncertain times: What can decision sciences say about or learn from economic crises?
  • Controlling uncertainty in multistep decision scenarios: Human behaviour in complex dynamic environments.
  • Invariants of human behaviour: Heuristic decision-making and learning strategies.
  • Cognitive approaches to rationality: Instrumental rationality or capacity to choose?
  • Neuro-computational models of social decision-making and learning: Where do we stand?
  • Multiobjective optimum design methods and multicriteria decision-making methods.
  • Players’ beliefs: Investigating coordination mechanisms in cooperative and non-cooperative games.
  • The role of leadership in team production and other managerial dilemmas: Political leadership in hierarchies.
  • Norm-generating structures: An alternative approach to the generation and maintenance of norms.
  • Cooperation and rationality: Notes on the collective action problem and its solutions.
  • Simon’s behavioural model of rational choice: Incorporating bounded rationality in economic models.
  • Rational recipes in action for a society composed predominantly of short-sighted and selfish individuals.
  • Normatively understood rationality: On what grounds do we distinguish acceptable degrees of stability?
  • Rational recipes under the sustainability eye: Trajectories in the current global challenges.
  • Interlinking cognitive psychology, economics, and computer science: An appraisal.
  • Heuristic decision-making in the ESG context: Bringing together simple rules and data-driven mathematics.
  • Cognitive building blocks: Theories of decision making for basic claims underlying economic analysis of law.
  • Search vs. omniscience: Aspiration level theories and fast and frugal heuristics.
  • Multi-objective programming and its application in quantitative social and cognitive sciences.
  • Resolving intergenerational conflicts over the environment according to rational choice view: Myth or reality?
  • Representing the distribution of wealth: From standard analyses to cognitive studies.
  • Novel scholarly journeys departing from standard economics: A one-way ticket.
  • Examining alternative frameworks characterised by unmanageable degrees of complexity.
  • Preference structure in data analysis and multiple objective linear programming.
  • A fundamentally uncertain real-life: From rational choice theory to evolutionary patterns of change.
  • When decision-making is decentralised: Time-constraint and availability of information in decision processes.
  • Games of partial information revolving around the concept of equilibrium.
  • Evolutionary game theory versus inclusive fitness theory: Modelling behavioural evolution.
  • Efficiency considerations or sufficiency aspects and social welfare functions: A fairy-tale ending?
  • Expert systems with applications: Multi-objective optimisation problems.
  • Modelling approaches to welfare economics, social choice theory, and theory of justice.
  • Conceptualising and measuring well-being using statistical semantics and numerical rating scales.
  • Which rationality exists in the landscape of global climate governance and fiscal balance?
  • Cognitive predicates of personal taste as linguistic devices used to convey perspectival information.
  • Failing of the efficient market hypothesis because of homothetic properties and the market “memory”.
  • Ranking of transitions as a psychologically plausible and scientifically legitimate way of preserving decreasing marginal utility vs. ranking as empirically unverifiable and superfluous to demand theory.
  • Equilibrium as a distribution of strategies? Setting evolutionary stable strategies in a meta-modelling context.
  • Multi-attribute decision-making methods: Alternative objectives and solutions, and composite scores.
  • Meta-heuristic multi-objective search approaches: Gait generation and meta-heuristic algorithms.
  • Failing of ideas surrounding the risk of financial operations within the randomness of market fluctuations and the normal distribution of returns.
  • Economic policy views: Rational vs. adaptive expectations, rational vs. irrational beliefs in a complex world.
  • Analysing decision-making in process control: Multidisciplinary approaches to understanding human performance in complex tasks.
  • Business model dynamics and change management: Cause and rationale of business decisions.
  • What economists and other social scientists have learned about dealing with descriptive/normative rationality and how they may drive future research progress.
  • Heuristics and decision-making for a post-EUT choice-centred view: A foregone conclusion?
  • Understanding cooperative behaviour: EUT framework vs. evolutionary models of cooperation.
  • Getting the upper hand: Cross-disciplinary nature of much modern statistical research.
  • Bell, book and candle: Reason, sentiment, and the rational-irrational dichotomy.
  • Advancing microeconomics analysis: Meta-theoretical development in economics.
  • Business model dynamics and change management: Replications and advancements.
  • A bolt from the blue: Cause and rationale of business behaviour and business decisions.
  • Coping with decisions and uncertainty: A post-neoclassical decision-making perspective.
  • Evolving concepts of optimality in strategic games: Multi-agent systems and joint strategies.
  • Organisations, societies, and the economies as dynamic evolving systems: Paces and characters of change.
  • Simultaneous choices of maximal pure strategies: Equilibrium points in games with vector payoffs.
  • Ordinal preferences over outcomes: Individual boundedly rational agents and n-person coalitions.
  • Statistical semantics: Describing and measuring social phenomena through natural language.
  • Applying statistics in social research: Cognitive aspects of naturally occurring [big] data.
  • Optimality or efficiency in applied mathematics and computer science concerned with decision making.
  • Satisficing approaches to eliciting risk preferences: Framing choices over risky payoff distributions.
  • Modelling multi-agent constraint systems: Satisfying user preferences while negotiating.
  • Decisions with multiple objectives: The emergence of inductive probabilities?
  • Satisficing under uncertainty: Leading to cooperation in mutual interests games.
  • Between hope and fear: The psychology of risk and reward in shaping investment decisions.
  • Satisficing within decision-making: Applications to engineering and computer science.
  • Is Bayesian decision theory really the final word on how rational agents should make decisions?
  • Methodological and theoretical foundations in economics: The positive, normative, and ontology of social problems.
  • Future of judgment and decision-making research: Developing frameworks establishing connections with studies on cognition as well as on social and institutional factors.
  • General equilibrium or ever-changing structural stability? The emergence of the systemic [or induced] nature of economic crises.
  • Aggregation of conflicting information coming from multiple sources: From rational choice theory to knowledge representation in databases.
  • Preferences as subjective evaluation of alternatives: Bundles of goods as vectors, household production functions.
  • General equilibrium or ever-changing structural stability? The systemic or induced nature of economic crises.
  • Does information processing generate a partition? Partitional models, non-partitional models, and axiomatic approaches.
  • The rich domain of uncertainty: Applying heuristic decision-making approaches to heterogeneous agent models.
  • "As if" maximising expected utility involves assuming rational preferences, knowing utilities and the stochastic relation between actions and outcomes: Myth or reality?
  • Individual decision-making vs market-level predictions: What happens to rational choice in experimental markets?
  • Modelling bounded rationality as stemming from limited information processing: computability, automata, perceptrons, and optimal networks.
  • Uncertainty in various [dis]guises: Dealing with dynamic decision problems when knowledge of the environment is limited.

Originating in 2015, DECON embodies original and comparative research, as well as annual follow-up assessments on decision-making and economics, aggregate outcomes, and implications for social and cognitive sciences. It involves several international research institutions and over three hundred authors and academic departments. In the continuing spirit of international cooperation, the steering and organising Committee of DECON issues this Call for Papers, culminating in the annual three-day Symposium to be held in hybrid form in the World Heritage Site of Salamanca.


In addition to the main conference track, six special sessions are also planned to enable insights into economics, decision-making, cognitive sciences, and related subjects. They will represent further highlights of the sixth edition of Decision Economics by connecting a dynamic and interdisciplinary group of researchers to the Conference:

(i) “AI-driven Decision Making” (AIDM) is an immersive session organised and coordinated by Stefano Za and Marco Smacchia (University of Chieti-Pescara, Italy), and Michele Cipriano (Catholic University of the Sacred Heart - Piacenza). One of the interests of this special track is related to the design and evaluation of AI-driven decision making. At the same time, this track also raises the question of whether and how relatively more rational and mindful decision-making could depend on human-driven and AI-driven policy influence. 

(ii) “Ethics of Decision-Making in Economics” (EDME) is a consolidated session organised and coordinated by Tony E. Persico (Georgia Institute of Technology, Atlanta, United States), Tony Guidotti (Harvard University), and Elizabeth Garlow (New America Foundation). The session aims to discuss and understand today’s ethical challenges in political economy and economic policy, focusing on economic methodology and the philosophy of science and economics.

(iii) “Smart Heuristics for Civil Servants” (SMAHCS), launched by Rino Rumiati (University of Padua) and Davide Pietroni (University of Chieti-Pescara), is a thought-provoking conference session that explores various topics related to effectively engaging with the complexity and uncertainty inherent in the issues the civil service and public administration institutions face. 

(iv) “Economics, Law and Psychology in Taxation: Perspectives and Critical Issues of Behavioral Taxation” (BETA) by Salvatore Villani and Loredana Strianese (University of Naples Federico II) is a novel conference session aimed at disseminating recent advancements and emerging trends in the psychological analysis of economic behaviour pioneered by scholars such as George Katona.

(v) “Socio-Behavioural Research and Human-Centred Design” (SOCBER) by Ionut Virgil Serban (University of Craiova) and Gianmarco Cifaldi (University of Chieti-Pescara) is a novel conference session intended to highlight behavioural science on decision-making and design research, understanding the stakeholders’ perspectives to help policies and programmes respond properly to human evolving needs.

(vi) “Decision-Making for Public Sector Recovery” (DEPSER) by Simone Cifolelli and Lorenzo Fabiani (University of Chieti-Pescara) is a novel session that aims to broaden perspectives and inspire action to reaffirm the strategic role of the State, regulation, local government, and public finance in the economy of uncertainty.

For further insights, see


All papers must be formatted according to the SSDC template, with a maximum length of 10 pages (minimun 4 pages) including figures and references:


All proposed papers must be submitted in electronic form (PDF format) using the DECON conference management system.

Review process

DECON welcomes submissions with a preference for topics listed in the Call for Papers. All submitted papers will undergo a rigorous peer review process; each paper will be referred by at least three experts in the field, and be selected based on originality, quality, soundness, and relevance.

Special Issues

Authors of papers selected from DECON will be invited to submit an extended version to special issues featured across several journals:

Extended versions of some selected papers will be published in New Mathematics and Natural Computation (NMNC) [ISSN print: 1793-0057; ISSN online: 1793-7027]

Extended versions of selected papers will be published in the International Journal of Interactive Multimedia and Artificial Intelligence (ISSN: 1989 - 1660, JCR (2022): 3,6 (Q3))

Special Issue on the Journal Sensors: “Internet of Things (IoT) and Cloud Computing Technologies for Smart Cities and Rural Areas: Sensing and Information Management Technologies” published in MDPI Sensors Journal (ISSN: 1424-8220, JCR (2022): 3.9 (Q1))

Authors of selected papers will be invited to submit an extended and improved version to a Special Issue “Blockchain Applications in the Metaverse for Smart Cities” published in MDPI Smart Cities Journal (ISSN: 2624-6511, JCR (2022): 6.4 (Q1))

Authors of selected papers from PAAMS and Co-located Events will be invited to submit an extended and improved version to a Special Issue "Advanced Architectures for Hybrid Edge Analytics Models on Adaptive Smart Areas" published in MDPI Electronics Journal (ISSN: 2079-9292, JCR (2022): 2.9 (Q2))

Special Issue on the Journal Future Internet: “Deep Learning in Recommender Systems” published in MDPI Future Internet Journal (ISSN: 1999-5903, JCR (2022): 3.4)

Authors of selected papers will be invited to submit an extended and improved version to a Special Issue “Security in the Internet of Things (IoT)” published in MDPI Future Internet Journal (ISSN: 1999-5903, JCR (2022): 3.4)

Authors of selected papers from PAAMS and Co-located Events will be invited to submit an extended and improved version to a Special Issue “Advancements in Practical Applications of Agents, Multi-Agent Systems and Digital Twins” published in MDPI Systems Journal (ISSN: 2079-8954, JCR (2022): 1.9 (Q2))

Authors of selected papers from DECON and Co-located Events will be invited to submit an extended and improved version to a Special Issue published in ADCAIJ (ISSN: 2255-2863, JCR (2022): 1.4, JCI (2022): 0.09 (Q4)) indexed in DOAJ, ProQuest, Scholar, WorldCat, Dialnet, Sherpa ROMEO, Dulcinea, UlrichWeb, Emerging Sources Citation Index of Thomson Reuters, BASE y Academic Journals Database.

To Be Updated

General deadlines

  • Submissions

    10th June, 2024

  • Notification of acceptance

    14th June, 2024

  • Conference Celebration

    26th-28th June, 2024

Chairmen of the steering and organising committee

Edgardo Bucciarelli

Edgardo Bucciarelli, University of Chieti-Pescara (Italy)

Shu-Heng Chen

Shu-Heng Chen, National Chengchi University, Taipei (Taiwan)

Juan Manuel Corchado

Juan Manuel Corchado, University of Salamanca (Spain)

Javier Parra

Javier Parra, University of Salamanca (Spain)

DECON conference