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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.
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.
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.
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.
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.
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.
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
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.
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 first, preliminary result, is that geographical proximity may to some extent substitute for cognitive limitations.
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.
In this section I gather my publications on the methodology of social science.
Publications (in time order)
Pubblicazioni in Italiano
Institutional Web Site
Personal Web Site