guido.fioretti AT

The origin of Keynes' ideas on uncertainty

In the second half of the XIX century Johannes von Kries, a physiologist who was applying probability theory to the evaluation of the effectiveness of new drugs, realised that the computation of probability distributions depends on the classification of symptoms and pathologies into diseases. Confronted with a setting where the crucial uncertainty was the very definition of "events" by the experimenter, von Kries developed the logical foundations of a probability theory where the subjectivity of mental representations may impair the possibility of assigning numerical values to probabilities. With a series of distortions and misunderstandings, von Kries's ideas passed on to Keynes and formed the core of his economics.


  1. John Maynard Keynes and Johannes von Kries. History of Economic Ideas, 6 (3) 1998 : 51-80. abstract
  2. Von Kries and the Other "German Logicians": Non-numerical probabilities before Keynes. Economics and Philosophy, 17 (2) 2001: 245-273. abstract
  3. No Faith, No Conversion: The evolution of Keynes's ideas on uncertainty under the influence of Johannes von Kries. In Jochen Runde and Sohei Mizuhara (eds.), Perspectives on the Philosophy of Keynes's Economics: Probability, Uncertainty and Convention, Chapter X, London, Routledge 2003. abstract
  4. Analogies, Conventions and Expert Systems in Medicine: Some insights from a XIX century physiologist. In Ray Paton and Laura McNamara (eds.), Multidisciplinary Approaches to Theory in Medicine, Studies in Multidisciplinarity, Volume III, Chapter VIII, pp. 131-145. Amsterdam, Elsevier 2006. abstract
  5. Johannes von Kries on Cognition In Gerhard Wagner (ed.), The Range of Science: Studies on the interdisciplinary legacy of Johannes von Kries, Studies in Cultural and Social Sciences, Volume XIX, Chapter III, pp. 65-77. Wiesbaden, Harrassowitz 2019. abstract

Shackle and Shafer

In the 1950s the economist George Shackle outlined the features of a decision theory that would account for human behavior in the face of unforeseen contingencies. In the 1970s the mathematician Glenn Shafer initiated Evidence Theory, that extends and formalizes many of Shackle's intuitions. The prototypical situation of Evidence Theory is not a gambler throwing dice, but a judge or detective evaluating testimonies. In businesses like in detective stories, it is crucial to take account of unforeseen contingencies. My work on this subject consists of bridging between Shackle and Shafer, illustrating the principles of Evidence Theory to social scientists.


  1. A Mathematical Theory of Evidence for G.L.S. Shackle. Mind and Society, 2 (1) 2001: 77-98. abstract
  2. Evidence Theory: A Mathematical Framework for Unpredictable Hypotheses. Metroeconomica, 55 (4) 2004: 345-366. abstract
  3. Evidence Theory as a Procedure for Handling Novel Events . Metroeconomica, 60 (2) 2009: 283-301. abstract
  4. Either, Or: Exploration of an Emerging Decision Theory. IEEE Transactions on Systems, Man, and Cybernetics C, 42 (6) 2012: 854-864. abstract
  5. Utility, Games, and Narratives. In Bruce Edmonds and Ruth Meyer (eds.), Simulating Social Complexity: A Handbook, Series: Understanding Complex Systems, Chapter XIII, pp. 293-334. Berlin-Heidelberg, Springer Verlag 2013. Reprinted in 2017 as Chapter XVI, pp. 369-409. abstract

Deciding not to decide

Liquidity preference, so relevant for investment decision making and credit rationing, is an instance of deciding not to make any decision. This is not an option to be evaluated with respect to other alternatives, but rather stems from recognizing that novel contingencies disrupted any confidence in previously held mental models. Thus, deciding not to decide can be seen as originating from too intricate cognitive maps caused by unexpected causal relations. By means of a computational model of the intricacy of cognitive maps it is possible to simulate visionary investment decisions, wait-and-see attitudes, the arousal of confidence and its disruption.


  1. A Concept of Complexity for the Social Sciences. Revue Internationale de Systémique, 12 (3) 1998: 285-312. abstract
  2. A Subjective Measure of Complexity. Advances in Complex Systems, 2 (4) 1999: 349-370. abstract
  3. A Cognitive Interpretation of Organizational Complexity (with Bauke Visser). Emergence: Complexity and Organization, 6 (1-2) 2004: 11-23. abstract
  4. A Model of Primary and Secondary Waves in Investment Cycles. Computational Economics, 24 (4) 2004: 357-381. abstract
  5. Credit Rationing with Symmetric Information. Economie Appliquée, 61 (3) 2009: 5-34. abstract

Empirical studies of innovations

Innovation is the realm where my research on decision-making under uncertainty can find applications. I eventually undertake field studies of specific technological innovations, particularly those triggering unexpected interactions with social actors and their power structures.


  1. Playfulness, Ideology and the Technology of Foolishness in the Creation of a Novel Market Niche for Distributed Control: The case of iPLON. Journal of Organizational Design, 5 (6) 2016. abstract

The Garbage Can model of organizational decision-making

The Garbage Can model by Cohen, March and Olsen is by far the most influential model of organizational decision-making. It was presented as a simulation model implemented on procedural code, but it describes decision-making as resulting from the interactions of four kinds of agents: 'participants', 'opportunities', 'solutions' and 'problems'.
By implementing the Garbage Can model as an agent-based model we have been able to derive its most interesting properties from first principles, rather than encoding them explicitely as in the original version. Furthermore, the greater clarity imposed by the agent-based representation suggested a deeper understanding of the model, its limits and its implications.


  1. An Agent-Based Representation of the Garbage Can Model of Organizational Choice (with Alessandro Lomi). Journal of Artificial Societies and Social Simulation, 11 (1) 2008. abstract
  2. The Garbage Can Model of Organizational Choice: An Agent-Based Reconstruction (with Alessandro Lomi). Simulation Modelling Practice and Theory, 16 (2) 2008: 192-217. abstract
  3. Passing the Buck in the Garbage Can Model of Organizational Choice (with Alessandro Lomi). Computational and Mathematical Organization Theory, 16 (2) 2010: 113-143. abstract
  4. Garbage Can Ecologies: An Agent-Based Exploration. In Alessandro Lomi and J. Richard Harrison (eds.), The Garbage Can Model of Organizational Choice: Looking Forward at Forty, Research in the Sociology of Organizations, Volume XXXVI, Chapter VI, pp. 141-164. Bingley, Emerald Group Publishing 2012. abstract

Recognition of innovations by means of neural networks

Investing in novel fields requires the ability of recognizing the potentiality of innovations. Indeed, much of the difference between successful amd unsuccessful firms depends on this.
Recognition of innovation is an instance of pattern recognition, which can be reproduced by neural networks. In particular, Kohonen's self-organizing maps are able to reproduce the formation of mental categories for classifying novel items.


  1. Business Cycle Dynamics in a Neural Net Framework (with Bernd Süßmuth). International Journal of Computer Research, 10 (2) 2001: 201-222. abstract
  2. The Investment Acceleration Principle Revisited by means of a Neural Network. Neural Computing and Applications, 13 (1) 2004: 16-23. abstract
  3. Recognising Investment Opportunities at the Onset of Recoveries. Research in Economics, 60 (2) 2006: 69-84. abstract

Organizations as self-organizing networks

Organizations can be seen as networks of relations where collective behavior emerges out of interactions of single components. This vision produced a rather diverse set of investigations, including (a) a revisitation of the production function by means of systems theory and connectionist concepts; (b) an understanding of vacancy chains as a means for allocating resources, alternative to markets; (c) a formalization of the idea of "flexible organization" by means of a concept borrowed from physics and biology; (d) a formalization of the emergence of routines, and (e) an investigation of the relations between individual intelligence and organizational intelligence.


  1. The Production Function. Physica A, 374 (2) 2007: 707-714. abstract
  2. A Model of Vacancy Chains as a Mechanism for Resource Allocation. Journal of Mathematical Sociology, 34 (1) 2010: 52-75. abstract
  3. Two Measures of Organizational Flexibility. Journal of Evolutionary Economics, 22 (5) 2012: 957-979. abstract

The organizational learning curve

In many industries, particularly airframe and shipbuilding, it has been observed that production time tends to decrease with the number of items produced. However, the rate of this decrease is far from predictable, and sometimes it did not occur at all.
Viewing organizations as self-organizing networks opens a possibility for understanding the arousal of organizational learning curves as emergence of routines. I designed theoretical models, carried out simulations and observed some empirical cases.


  1. The Organizational Learning Curve. European Journal of Operational Research, 177 (3) 2007: 1375-1384. abstract
  2. A Connectionist Model of the Organizational Learning Curve. Computational and Mathematical Organization Theory, 13 (1) 2007: 1-16. abstract
  3. From Men and Machines to the Organizational Learning Curve. In Mohammad Y. Jaber (ed.), Learning Curves: Theory, Models, and Applications, Chapter IV, pp. 57-70. Boca Raton, CRC Press 2011. abstract

Prato and other industrial districts

Prato, central Italy, has been purported as the prototypical example of a system of small firms taking advantage of collaboration networks enabled by geographical proximity. My investigations suggest that other factors may be prominent, such as exploitation of cheap labor and avoidance of environmental regulations, but also the flexibility enabled by a large number of firms both in terms of quick adjustment to production needs and, most importantly, the ability to produce a large variety of goods. This last aspect is particularly important in a textile industrial district such as Prato.
To a large extent, I made use of agent-based models. This kind of simulation technique is very appropriate to investigate the dynamics of a large number of interacting firms.
In one instance, I analyzed the qualitative features of firms' web sites.


  1. Information Structure and Behaviour of a Textile Industrial District. Journal of Artificial Societies and Social Simulation, 4 (4) 2001. abstract
  2. Will Industrial Districts Exploit B2B? A local experience and a general assessment. NetNomics, 6 (3) 2004: 221-242. abstract
  3. Agent-Based Models of Industrial Clusters and Districts. In Albert Tavidze (ed.), Progress in Economics Research, Vol. IX, Chapter VIII, pp. 125-142. New York, Nova Science Publishers 2006. abstract
  4. Trajectories in Geographical Space out of Communication in Acquaintance Space: An agent-based model of a textile industrial district. Papers in Regional Science, 93 (S1) 2014: S179-S201. abstract

Knowledge networks among small firms

I built a large agent-based model where boundedly rational small firms compete in geographical space, developing their knowledge bases and undertaking a variety of strategy. A crucial assumption of this model is that consumers value those innovations that bridge between existing knowledge, which is a statement on the origin of tastes. A first, preliminary result, is that geographical proximity may to some extent compensate cognitive limitations.


  1. Rivalry and Learning Among Clustered and Isolated Firms (with Cristina Boari and Vincenza Odorici). In Edoardo Mollona (ed.), Computational Analysis of Firms' Organization and Strategic Behaviour, Routledge Series in Organizational Behaviour and Strategy, Volume VI, Chapter VII, pp. 171-192. New York, Routledge 2010. abstract
  2. A Model of Innovation and Knowledge Development Among Boundedly Rational Rival Firms (with Cristina Boari and Vincenza Odorici). Team Performance Management, 23 (1-2) 2017: 82-95. abstract

On the maximum size of human groups

By evaluating the cognitive stress of the members of groups of different size and interaction structure, I arrive at maximum thresholds of 5-6 persons for plain groups, 15 persons for centered groups and 50-100 persons for federative groups, respectively. Beyond each threshold, a group either dissolves or modifies its structure according to the requirements of the subsequent class. My theory is in accord with empirical findings from psychology, anthropology and organization science.
Essentially, this is an instantiation of the concept of bounded rationality. It has a number of consequences for the management of teams, committees and communities of practice. Furthermore, it explains several puzzles in sociology and macroeconomics.


  1. The Small World of Business Relationships. In Jack Birner and Pierre Garrouste (eds.), Markets, Information and Communication: Austrian perspectives on the Internet economy, Chapter VIII, pp. 189-198. London, Routledge 2004. abstract

Methodological papers

In this section I gather my publications on the methodology of social science.


  1. Agent-Based Simulation Models in Organization Science. Organizational Reseach Methods, 16 (2) 2013: 227-242. abstract
  2. Emergent Organizations. In Davide Secchi and Martin Neumann (eds.), Agent-Based Simulation of Organizational Behavior, Chapter II, pp. 19-41. Berlin-Heidelberg, Springer Verlag 2016. abstract

Publications (in time order)

Pubblicazioni in Italiano

Working Papers

Institutional Web Site

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