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WIVACE 2016: the Workshop on Artificial Life and Evolutionary Computation

Salerno | 4-5 October 2016

Riccardo Righi presents "New paths for the application of DCI in social sciences: theoretical issues regarding an empirical analysis", in collaboration with Andrea Roli, Margherita Russo, Roberto Serra e Marco Villani

The present edition has the ambition to renovate the spirit of Wivace and Bionam past editions and to add an extra layer of hybridization. Wivace will take place the first 3 days (4-6 October 2016) and Bionam the last two days (6-7 October 2016). Therefore, there will be complete superimposition on October 6th. This double event will permit researchers active on bioinspired algorithms, the Wivace community, to get together and to get in touch with researchers working on bioinspired materials, the Bionam community, to create a exciting cross-over of disciplines and approaches.
book of abstract on line

Riccardo Righi1 , Andrea Roli2 , Margherita Russo1 , Roberto Serra3 and Marco Villani3 1 Department of Economics, University of Modena and Reggio Emilia {riccardo.righi, margherita.russo}
2 Department of Computer Science and Engineering, University of Bologna
3 Department of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia {rserra, marco.villani}

Extended abstract 
Dynamic Cluster Index analysis (DCI) takes its origin from the neurological studies of Giulio Tononi in the 90’s. Tononi supposed that neurons with similar functions show high level of coordination in their behaviors over time, independently from being, or not, situated in the same brain region1 . Tononi introduced the notion of ‘functional cluster’, defining it as “a set of elements that are much more strongly interactive among themselves than with the rest of the system, whether or not the underlying anatomical connectivity is continuous” (Tononi 1997). Functional clusters should have had an internal exchange of information (among neurons belonging to the same group) stronger than the exchange of information that the same neurons have with the rest of the system. Taking advantage of two information theory concepts (integration and mutual information), Tononi introduced a new concept: the ‘cluster index’ (CI) (Tononi et al. 1994). The study of CI demonstrated that neurons with integrated profiles of activity over time (i) have similar functions and (ii) have a location that is independent from anatomical proximity.
Following this pioneering contribution, Villani et al. (2013a, 2013b) developed an algorithm for the detection of subsets of agents introducing the comparison between the CI of an observed subset and the CI of an homogeneous system2 . Thanks also to the introduction of an heuristic procedure (Villani et al. 2015), the established algorithm, named by the authors ‘Dynamic Cluster Index’ (DCI), is able to produce a final ranking of all possible subsets that can be considered in any initial set. Without considering any information about the topology of the network, the pattern of the behaviors of the agents is the only information that is used in the process of subset detection. DCI up to now has been tested in research areas of artificial network models, of catalytic reaction networks and of biological gene regulatory systems (Villani et al. 2013a, Villani et al. 2013b, Filisetti et al. 2011), giving an important and recognized contribution to the problem of identifying emergent meso-level structures (Villani et al. 2013a).
The creation and the implementation of the DCI algorithm opens new paths for addressing socio-economic problems regarding the analysis of group of agents. Up to now the detection and the analysis of communities typically have been performed through the consideration of similar characteristics of agents, or through the analysis of the observed network structure. Indeed, DCI methodology makes possible to shift the attention into a new dimension of organizations of agents: the presence of a common function characterizing their actions. In this paper we discuss the implications of the use of this methodology in the domain of social sciences, with specific reference to the application to an empirical analysis.
In Section 1 we propose an overview of the theoretical elements of the CI proposed by Tononi et al. (1994, 1996, 1997, 1998) and of the DCI as proposed by Villani et al. (2013a, 2013b, 2015). In Section 2 we present the specific case study, regarding network innovation policies that Tuscany Region (Italy) implemented in the programming period 2000-2006. In Section 3 we describe the advantages of applying DCI in a context where the application of Complex Network modeling of community detection come up against the absence of stepwise processes of formation/dissolution of relational structures. In Section 4 we describe theoretical considerations regarding the application of DCI in a socioeconomic context of analysis. In Section 5 we discuss the problem of how to define agents’ activity. In Sec. 6 we underline the potentiality of DCI analysis to investigate unobserved relations. Conclusions summarize the investigation of functional communities (group of agents that share a common function) in the landscape of community detection techniques, and highlight the potentialities of the application of DCI in socio-economic analyses aimed at detecting emerging functional communities.


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The proceedings of the WIVACE 2016 edition, will be published in the Springer book series " Communications in Computer and Information Science ".