The Laboratory of Agent Based Social Simulation at the Institute of Cognitive Sciences and Technologies CNR is seeking candidates for a one-year post-doc position (renewable up to 3-years) to work on the EU Horizon 2020 project ‘PROTON’ for simulative research on the evolution of organized crime and terrorist networks.
The SOCIAL SIMULATION CONFERENCE, SSC 2016, the 6th joint meeting of ESSA, PAAA, and CSSSA, will be held in Rome, September 19-23, 2016.
We cordially invite researchers and practitioners from all over the world to Rome, “caput mundi”, to share their latest results and foster our understanding of social complexity problems by means of computational simulations.
SSC2016 will be the 6th joint meeting (previously known as WCSS) of the three main scientific societies for Social Simulation, Social Systems Science and Computational Social Science.
Rethinking Economy and the Social Science with multi-disciplinar simulations (in Italian; click here for a Google translation to English.
Mathematical modeling: dynamics of nonlinear systems and chaos.
Aprile, 16. 2015
This course is designed to be a bridge between the study of mathematics and the application of mathematics to various fields. It provides an overview of how the mathematical pieces of an applied problem fit together.
Mathematical modeling is the process of creating a mathematical representation of some phenomenon in order to gain a better understanding of that phenomenon. The main goal of this course is to learn how to make creative use of some mathematical tools, such as difference equations, ordinary and partial differential equations, and numerical analysis, to build a mathematical description of biological, social and economic phenomena.
For info/scheduling click HERE or mail to firstname.lastname@example.org
We are delighted to announce that Luis Gustavo Nardin has obtained the “best student paper award at SSC 2014” with the paper From Anarchy to Monopoly: How Competition and Protection Shaped Mafia’s Behavior
We’re glad to announce a new paper from LABSS as a contribution to the debate on the approach to computational social science: On Agent-Based Modelling and Computational Social Science.
In the first part of the paper, the field of Agent-Based Modelling is discussed focusing on the role of generative theories, aiming at explaining phenomena by growing them. After a brief analysis of the major strengths of the field some crucial weaknesses are analysed. In particular, the generative power of ABM is found to have been underexploited, as the pressure for simple recipes has prevailed and shadowed the application of rich cognitive models. In the second part of the paper, the renewal of interest for Computational Social Science is focused upon, and several of its variants, such as deductive, generative, and complex CSS, are identified and described. In the concluding remarks, an interdisciplinary variant, which takes after ABM, reconciling it with the quantitative one, is proposed as a fundamental requirement for a new program of the CSS.
New paper on peer review from LABSS and UniValencia: Mechanism change in a simulation of peer review: from junk support to elitism.
Our honest, totally unbiased, objective evaluation of this work is: reading it will change your life. You will sleep better. A sense of clarity will ensue. The pictures will spring up your imagination. The only paper you really need to read this year.
Ahem. Well maybe we’re a little bit overplaying it. Ok, here’s the abstract:
Peer review works as the hinge of the scientific process, mediating between research and the awareness/acceptance of its results. While it might seem obvious that science would regulate itself scientifically, the consensus on peer review is eroding; a deeper understanding of its workings and potential alternatives is sorely needed. Employing a theoretical approach supported by agent-based simulation, we examined computational models of peer review, performing what we propose to call redesign, that is, the replication of simulations using different mechanisms. Here, we show that we are able to obtain the high sensitivity to rational cheating that is present in literature. In addition, we also show how this result appears to be fragile against small variations in mechanisms. Therefore, we argue that exploration of the parameter space is not enough if we want to support theoretical statements with simulation, and that exploration at the level of mechanisms is needed. These findings also support prudence in the application of simulation results based on single mechanisms, and endorse the use of complex agent platforms that encourage experimentation of diverse mechanisms.
We are glad to point at the special issue of CNR “Almanacco della Scienza” on the winners of the Ricercat@mente prize : in particular our Giulia Andrighetto with her research on norms (in Italian).
New paper published: The Norm-Signaling Effects of Group Punishment: Combining Agent-Based Simulation and Laboratory Experiments
Daniel Villatoro, Giulia Andrighetto, Jordi Brandts, Luis Gustavo Nardin, Jordi Sabater-Mir, and Rosaria Conte: The Norm-Signaling Effects of Group Punishment: Combining Agent-Based Simulation and Laboratory Experiments Social Science Computer Review 0894439313511396, first published on December 11, 2013
Punishment plays a crucial role in favoring and maintaining social order. Recent studies emphasize the effect of the norm-signaling function of punishment. However, very little attention has been paid so far to the potential of group punishment. We claim that when inflicted by an entire group, the recipient of punishment views it as expressing norms. The experiments performed in this work provide evidence that humans are motivated not only by material incentives that punishment imposes but also by normative information that it conveys. The same material incentive has a different effect on the individuals’ future compliance depending on the way it is implemented, having a stronger effect when it also conveys normative information. We put forward the hypothesis that by inflicting equal material incentives, group punishment is more effective in enhancing compliance than uncoordinated punishment, because it takes advantage of the norm-signaling function of punishment. In support of our hypothesis, we present cross-methodological data, that is, data obtained through agent-based simulation and laboratory experiments with human subjects. The combination of these two methods allows us to provide an explanation for the proximate mechanisms generating the cooperative behavior observed in the laboratory experiment.