L'azione economica tra etica e neurobiologia

The heated debate over the conflict between ethics and economics is often described as an epochal issue, an expression of present-day fragility, resulting from the implosion of the development model, which has characterised western society. The debate, however, exposes a paradox. Whilst, on the one hand, the neoclassical economic theory is radically criticized, on the other such criticism does not appear to delineate any solid, practicable alternative. Thus, the mainstream economic theory is still taught, practised by individuals as well as institutions, and further developed by the prevailing academic research. For this reason, a viable alternative needs to be sought, along with a new research methodology, which would allow to apply novel and more coherent theoretical assumptions into effective research and real cases. The theoretical instruments by which to create the models for human behaviour need to take into account the biological foundation of behaviour, expressed in evolutionary genetics terms. The aim of this paper is to establish whether our moral knowledge of economics may claim any scientific objectivity in light of advances in subject areas that differ in their scope and methods: moral philosophy, economics, cognitive neuroscience and artificial intelligence, each of which makes a specific contribution to understanding the operation of the human mind and towards forming the moral values onto which economic choice and action are founded. Given that the object of the study of economic science is the analysis of complex systems, nowadays the most efficient method seems to be artificial life simulation.

Reti complesse e analisi del segnale elettroencefalografico

The identification of subject-specific traits extracted from patterns of brain activity still represents an important challenge. The need to detect distinctive brain features, which is relevant for biometric and brain computer interface systems, has been also emphasized in monitoring the effect of clinical treatments and in evaluating the progression of brain disorders. Graph theory and network science tools have revealed fundamental mechanisms of functional brain organization in resting-state M/EEG analysis. Nevertheless, it is still not clearly understood how several methodological aspects may bias the topology of the reconstructed functional networks. In this context, the literature shows inconsistency in the chosen length of the selected epochs, impeding a meaningful comparison between results from different studies. In this study we propose an approach which aims to investigate the existence of a distinctive functional core (sub-network) using an unbiased reconstruction of network topology. Brain signals from a public and freely available EEG dataset were analyzed using a phase synchronization based measure, minimum spanning tree and k-core decomposition. The analysis was performed for each classical brain rhythm separately. Furthermore, we aim to provide a network approach insensitive to the effects that epoch length has on functional connectivity (FC) and network reconstruction. Two different measures, the phase lag index (PLI) and the Amplitude Envelope Correlation (AEC), were applied to EEG resting-state recordings for a group of eighteen healthy volunteers. Weighted clustering coefficient (CCw), weighted characteristic path length (Lw) and minimum spanning tree (MST) parameters were computed to evaluate the network topology. The analysis was performed on both scalp and source-space data. Results about distinctive functional core, show highest classification rates from k-core decomposition in gamma (EER=0.130, AUC=0.943) and high beta (EER=0.172, AUC=0.905) frequency bands. Results from scalp analysis concerning the influence of epoch length, show a decrease in both mean PLI and AEC values with an increase in epoch length, with a tendency to stabilize at a length of 12 seconds for PLI and 6 seconds for AEC. Moreover, CCw and Lw show very similar behaviour, with metrics based on AEC more reliable in terms of stability. In general, MST parameters stabilize at short epoch lengths, particularly for MSTs based on PLI (1-6 seconds versus 4-8 seconds for AEC). At the source-level the results were even more reliable, with stability already at 1 second duration for PLI-based MSTs. Our results confirm that EEG analysis may represent an effective tool to identify subject-specific characteristics that may be of great impact for several bioengineering applications. Regarding epoch length, the present work suggests that both PLI and AEC depend on epoch length and that this has an impact on the reconstructed network topology, particularly at the scalp-level. Source-level MST topology is less sensitive to differences in epoch length, therefore enabling the comparison of brain network topology between different studies.