Simone Righi

Weighted Fractal Networks

In this paper we define a new class of weighted complex networks sharing several properties with fractal sets, and whose topology can be completely analytically characterized in terms of the involved parameters and of the fractal dimension. General networks with fractal or hierarchical structures can be set in the proposed framework that moreover could be used to provide some answers to the widespread emergence of fractal structures in nature.

EMOTIONAL STRATEGIES AS CATALYSTS FOR COOPERATION IN SIGNED NETWORKS

The evolution of unconditional cooperation is one of the fundamental problems in science. A new solution is proposed to solve this puzzle. We treat this issue with an evolutionary model in which agents play the Prisoners Dilemma on signed networks. The topology is allowed to co-evolve with relational signs as well as with agent strategies. We introduce a strategy that is conditional on the emotional content embedded in network signs. We show that this strategy acts as a catalyst and creates favorable conditions for the spread of unconditional cooperation. In line with the literature, we found evidence that the evolution of cooperation most likely occurs in networks with relatively high chances of rewiring and with low likelihood of strategy adoption. While a low likelihood of rewiring enhances cooperation, a very high likelihood seems to limit its diffusion. Furthermore, unlike in nonsigned networks, cooperation becomes more prevalent in denser topologies.

EMERGING STRUCTURES IN SOCIAL NETWORKS GUIDED BY OPINIONS' EXCHANGES

Modeling social phenomena represents a major challenge that has in recent years attracted a growing interest. Insight into the problem can be gained by resorting, among others, to the so called Agent Based Models, an approach that is well suited to bridge the gap between hypotheses concerning the microscopic behavior of individual agents and the emergence of collective phenomena in a population composed of many interacting heterogeneous entities. Constructing sound models deputed to return a reasonable approximation of the scrutinized dynamics is a delicate operation, given the degree of arbitrariness in assigning the rules that govern mutual interactions. In the vast majority of cases, data are scarce and do not sufficiently constrain the model, hence the providedIn this paper, we show that the small world and weak ties phenomena can spontaneously emerge in a social network of interacting agents. This dynamics is simulated in the framework of a simplified model of opinion diffusion in an evolving social network where agents are made to interact, possibly update their beliefs and modify the social relationships according to the opinion exchange.

The miracle of peer review and development in science: an agent-based model

It is not easy to rationalize how peer review, as the current grassroots of science, can work based on voluntary contributions of reviewers. There is no rationale to write impartial and thorough evaluations. If reviewers are unmotivated to carefully select high quality contributions, there is no risk in submitting low-quality work by authors. As a result, scientists face a social dilemma: if everyone acts according to his or her own self-interest, the outcome is low scientific quality. We examine how the increased relevance of public good benefits (journal impact factor), the editorial policy of handling incoming reviews, and the acceptance decisions that take into account reputational information, can help the evolution of high-quality contributions from authors. High effort from the side of reviewers is problematic even if authors cooperate: reviewers are still best off by producing low-quality reviews, which does not hinder scientific development, just adds random noise and unnecessary costs to it. We show with agent-based simulations why certain self-emerged current practices, such as the increased reliance on journal metrics and the reputation bias in acceptance, work efficiently for scientific development. Our results find no proper guidelines, however, how the system of voluntary peer review with impartial and thorough evaluations could be sustainable jointly with rapid scientific development.

What Does the Financial Market Pricing Do? A Simulation Analysis with a View to Systemic Volatility, Exuberance and Vagary

Biondi et al. (2012) develop an analytical model to examine the emergent dynamic properties of share market price formation over time, capable to capture important stylized facts. These latter properties prove to be sensitive to regulatory regimes for fundamental information provision, as well as to market confidence conditions among actual and potential investors. Regimes based upon mark-to-market (fair value) measurement of traded security, while generating higher linear correlation between market prices and fundamental signals, also involve higher market instability and volatility. These regimes also incur more relevant episodes of market exuberance and vagary in some regions of the market confidence space, where lower market liquidity further occurs.

Parallel versus Sequential Update and the Evolution of Cooperation with the Assistance of Emotional Strategies

Our study contributes to the debate on the evolution of cooperation in the single-shot Prisoners Dilemma (PD) played on networks. We construct a model in which individuals are connected with positive and negative ties. Some agents play sign-dependent strategies that use the sign of the relation as a shorthand for determining appropriate action toward the opponent. In the context of our model in which network topology, agent strategic types and relational signs coevolve, the presence of sign-dependent strategies catalyzes the evolution of cooperation. We highlight how the success of cooperation depends on a crucial aspect of implementation: whether we apply parallel or sequential strategy update. Parallel updating, with averaging of payoffs across interactions in the social neighborhood, supports cooperation in a much wider set of parameter values than sequential updating. Our results cast doubts about the realism and generalizability of models that claim to explain the evolution of cooperation but implicitly assume parallel updating.

Signed Networks, Triadic Interactions and the Evolution of Cooperation

We outline a model to study the evolution of cooperation in a population of agents playing the prisoners dilemma in signed networks. We highlight that if only dyadic interactions are taken into account, cooperation never evolves. However, when triadic considerations are introduced, a window of opportunity for emergence of cooperation as a stable behaviour emerges.

Inequality and the Financial Accumulation Process: A Computational Economic Analysis of Income and Wealth Dynamics

Our computational economic analysis investigates the relationship between inequality and the financial accumulation process in the study of income and wealth distributions. Extending the baseline model by Levy et al., we characterise the economic process trough featured return structures generating alternative stylised evolutions of income and wealth through historical time. First we explore the limited heuristic contribution of one and two factors models comprising one single stock (capital wealth) and one single flow factor (labour) as pure drivers of income and wealth generation and allocation over time. Then we introduce heuristic models of taxation and financial market pricing in line with the baseline approach. Our computational economic analysis corroborates that the financial accumulation process featuring compound returns plays a significant role as socioeconomic source of inequality, while institutional configurations play a significant role in framing and shaping the aggregate economic process that evolves over socioeconomic space and time.An updated version is available at: http://ssrn.com/abstract=2628536

Model selection and averaging in the assessment of the drivers of household food waste to reduce the probability of false positives

Food waste from households contributes the greatest proportion to total food waste in developed countries. Therefore, food waste reduction requires an understanding of the socio-economic (contextual and behavioural) factors that lead to its generation within the household. Addressing such a complex subject calls for sound methodological approaches that until now have been conditioned by the large number of factors involved in waste generation, by the lack of a recognised definition, and by limited available data. This work contributes to food waste generation literature by using one of the largest available datasets that includes data on the objective amount of avoidable household food waste, along with information on a series of socio-economic factors. In order to address one aspect of the complexity of the problem, machine learning algorithms (random forests and boruta) for variable selection integrated with linear modelling, model selection and averaging are implemented. Model selection addresses model structural uncertainty, which is not routinely considered in assessments of food waste in literature. The main drivers of food waste in the home selected in the most parsimonious models include household size, the presence of fussy eaters, employment status, home ownership status, and the local authority. Results, regardless of which variable set the models are run on, point toward large households as being a key target element for food waste reduction interventions.

BLIND VS. EMBEDDED INDIRECT RECIPROCITY AND THE EVOLUTION OF COOPERATION

The evolution of cooperation is one of the fundamental problems of both social sciences and biology. It is difficult to explain how a large extent of cooperation could evolve if individual free riding always provides higher benefits and chances of survival. In absence of direct reciprocation, it has been suggested that indirect reciprocity could potentially solve the problem of large scale cooperation. In this paper, we compare the chances of two forms of indirect reciprocity with each other: a blind one that rewards any partner who did good to previous partners, and an embedded one that conditions cooperation on good acts towards common acquaintances. We show that these two versions of indirect reciprocal strategies are not very different from each other in their efficiency. We also demonstrate that their success very much relies on the speed of evolution: their chances for survival are only present if evolutionary updates are not frequent. Robustness tests are provided for various forms of biases.