Giangiacomo Bravo

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Giangiacomo Bravo

Affiliations

Department of Economics and Statistics, University of Torino

Personal homepage

http://unito.academia.edu/GiangiacomoBravo

Professional homepage

http://unito.academia.edu/GiangiacomoBravo

ORCID more info

No associated ORCID account.

GitHub more info

No associated GitHub account.

No bio entered.

Agents’ beliefs and the evolution of institutions for common-pool resource management

Giangiacomo Bravo | Published Fri Dec 17 00:07:10 2010 | Last modified Sat Apr 27 20:18:21 2013

C++ and Netlogo models presented in G. Bravo (2011), “Agents’ beliefs and the evolution of institutions for common-pool resource management”. Rationality and Society 23(1).

Mast seeding model

Lucia Tamburino Giangiacomo Bravo | Published Sat Sep 8 05:54:44 2012 | Last modified Sat Apr 27 20:18:37 2013

Purpose of the model is to perform a “virtual experiment” to test the predator satiation hypothesis, advanced in literature to explain the mast seeding phenomenon.

We provide a full description of the model following the ODD protocol (Grimm et al. 2010) in the attached document. The model is developed in NetLogo 5.0 (Wilenski 1999).

Mobility USA (MUSA)

Davide Natalini Giangiacomo Bravo | Published Sun Dec 8 19:24:09 2013 | Last modified Mon Dec 30 19:22:17 2013

MUSA is an ABM that simulates the commuting sector in USA. A multilevel validation was implemented. Social network with a social-circle structure included. Two types of policies have been tested: market-based and preference-change.

NetLogo software for the Peer Review Game model. It represents a population of scientists endowed with a proportion of a fixed pool of resources. At each step scientists decide how to allocate their resources between submitting manuscripts and reviewing others’ submissions. Quality of submissions and reviews depend on the amount of allocated resources and biased perception of submissions’ quality. Scientists can behave according to different allocation strategies by simply reacting to the outcome of their previous submission process or comparing their outcome with published papers’ quality. Overall bias of selected submissions and quality of published papers are computed at each step.

Under development.

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