Foto del docente

Guido Fioretti

Associate Professor

Department of Management

Academic discipline: SECS-P/10 Organization and Human Resource Management


Keywords: Decision Theory Human groups Emergence of Structures Firm Networks Neural Networks Organizational Processes Cognitive Processes

  • The origin of Keynes' ideas on uncertainty
  • Shackle and Shafer (Evidence Theory)
  • Deciding not to decide
  • Recognition of innovations by means of neural networks
  • The Garbage Can model of organizational decision-making
  • Coordination, routines and learning in organizations
  • On the maximum size of human groups
  • Industrial clusters and networks

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 ubjectivity 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.

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.


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.


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.

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.


Coordination, routines and learning in organizations

Organizations can be seen as connectionist systems amenable to be studied by means of agent-based models and related formalisms. This way of looking at organizations yields interesting insights so far it concerns the emergence of routines, the features of organizational learning and the boundaries between markets and organizations.


On the 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 instance 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.

Industrial clusters and networks

Agent-based simulations and statistical network analysis are very powerful tools to study the emergence and evolution of local networks in terms of the structure of relationships between component firms, entrepreneurs, researchers and other economic agents. Among else, the impact of information and communication technologies and the formation of virtual clusters can be investigated.