For whom will the Bayesian agents vote?

Within an agent-based model where moral classifications are socially learned, we ask if a population of agents behaves in a way that may be compared with conservative or liberal positions in the real political spectrum. We assume that agents first experience a formative period, in which they adjust their learning style acting as supervised Bayesian adaptive learners. The formative phase is followed by a period of social influence by reinforcement learning. By comparing data generated by the agents with data from a sample of 15000 Moral Foundation questionnaires we found the following. 1. The number of information exchanges in the formative phase correlates positively with statistics identifying liberals in the social influence phase. This is consistent with recent evidence that connects the dopamine receptor D4-7R gene, political orientation and early age social clique size. 2. The learning algorithms that result from the formative phase vary in the way they treat novelty and corroborative information with more conservative-like agents treating it more equally than liberal-like agents. This is consistent with the correlation between political affiliation and the Openness personality trait reported in the literature. 3. Under the increase of a model parameter interpreted as an external pressure, the statistics of liberal agents resemble more those of conservative agents, consistent with reports on the consequences of external threats on measures of conservatism. We also show that in the social influence phase liberal-like agents readapt much faster than conservative-like agents when subjected to changes on the relevant set of moral issues. This suggests a verifiable dynamical criterium for attaching liberal or conservative labels to groups.Comment: 31 pages, 5 figure

Signatures of the neurocognitive basis of culture wars found in moral psychology data\ud

Moral Foundation Theory (MFT) states that groups of different observers may rely on partially dissimilar sets of moral foundations, thereby reaching different moral valuations on a subset of issues. With the introduction of functional imaging techniques, a wealth of new data on neurocognitive processes has rapidly mounted and it has\ud become increasingly more evident that this type of data should provide an adequate basis for modeling social systems. In particular, it has been shown that there is a spectrum of cognitive styles with respect to the differential handling of novel or corroborating information.\ud Furthermore this spectrum is correlated to political affiliation. Here we use methods of statistical mechanics to characterize the collective behavior of an agent-based model society whose interindividual interactions due to information exchange in the form of opinions, are in qualitative agreement with neurocognitive and psychological data. The main conclusion derived from the model is\ud that the existence of diversity in the cognitive strategies yields different statistics for the sets of moral foundations and that these arise from the cognitive interactions of the agents. Thus a simple interacting agent model, whose interactions are in accord with empirical data about moral dynamics, presents statistical signatures\ud consistent with those that characterize opinions of conservatives and liberals. The higher the difference in the treatment of novel and corroborating information the more agents correlate to liberals.\u

Statistical physics of error-correcting codes

In this thesis we use statistical physics techniques to study the typical performance of four families of error-correcting codes based on very sparse linear transformations: Sourlas codes, Gallager codes, MacKay-Neal codes and Kanter-Saad codes. We map the decoding problem onto an Ising spin system with many-spins interactions. We then employ the replica method to calculate averages over the quenched disorder represented by the code constructions, the arbitrary messages and the random noise vectors. We find, as the noise level increases, a phase transition between successful decoding and failure phases. This phase transition coincides with upper bounds derived in the information theory literature in most of the cases. We connect the practical decoding algorithm known as probability propagation with the task of finding local minima of the related Bethe free-energy. We show that the practical decoding thresholds correspond to noise levels where suboptimal minima of the free-energy emerge. Simulations of practical decoding scenarios using probability propagation agree with theoretical predictions of the replica symmetric theory. The typical performance predicted by the thermodynamic phase transitions is shown to be attainable in computation times that grow exponentially with the system size. We use the insights obtained to design a method to calculate the performance and optimise parameters of the high performance codes proposed by Kanter and Saad