arXiv subjects

José F. Fontanari

Publications and source records attributed to José F. Fontanari.

At least 37 records · Page 2Linked to original sources

arXiv2023

Cultural heterogeneity constrains diffusion of innovations

Rogers' diffusion of innovations theory asserts that cultural similarity among individuals plays a crucial role in the acceptance of an innovation in a community. However, most studies on the diffusion of innovations have relied on epidemic-like models where the individuals have no preference on whom they interact with. Here, we use an agent-based model to study the diffusion of innovations in a community of synthetic heterogeneous agents whose interaction preferences depend on their cultural similarity. The community heterogeneity and the agents' interaction preferences are described by Axelrod's model, whereas the diffusion of innovations is described by a variant of the Daley and Kendall model of rumour propagation. The interplay between the social dynamics and the spreading of the innovation is controlled by the parameter $p \in [0,1]$, which yields the probability that the agent engages in social interaction or attempts to spread the innovation. Our findings support Roger's empirical observations that cultural heterogeneity curbs the diffusion of innovations.

physics.soc-ph↗

arXiv2023

Local adaptation, phenotypic plasticity, and species coexistence

Understanding the mechanisms of species coexistence has always been a fundamental topic in ecology. Classical theory predicts that interspecific competition may select for traits that stabilize niche differences, although recent work shows that this is not strictly necessary. Here we ask whether adaptive phenotypic plasticity could allow species coexistence (i.e., some stability at an equilibrium point) without ecological differentiation in habitat use. We used individual-based stochastic simulations defining a landscape composed of spatially uncorrelated or autocorrelated environmental patches, where two species with the same competitive strategies, not able to coexist without some form of phenotypic plasticity, expanded their ranges in the absence of a competition-colonization trade-off (a well-studied mechanism for species diversity). Each patch is characterized by a random environmental value that determines the optimal phenotype of its occupants. In such a scenario, only local adaptation and gene flow (migration) may interact to promote genetic variation and coexistence in the metapopulation. Results show that a competitively inferior species with adaptive phenotypic plasticity can coexist in a same patch with a competitively superior, non-plastic species, provided the migration rates and variances of the patches' environmental values are sufficiently large.

q-bio.PE↗

arXiv2022

On the efficacy of the wisdom of crowds to forecast economic indicators

The interest in the wisdom of crowds stems mainly from the possibility of combining independent forecasts from experts in the hope that many expert minds are better than a few. Hence the relevant subject of study nowadays is the Vox Expertorum rather than Galton's original Vox Populi. Here we use the Federal Reserve Bank of Philadelphia's Survey of Professional Forecasters to analyze $15455$ forecasting contests to predict a variety of economic indicators. We find that the median has advantages over the mean as a method to combine the experts' estimates: the odds that the crowd beats all participants of a forecasting contest is $0.015$ when the aggregation is given by the mean and $0.026$ when it is given by the median. In addition, the median is always guaranteed to beat the majority of the participants, whereas the mean beats that majority in 67 percent of the forecasts only. Both aggregation methods yield a $20$ percent error on the average, which must be contrasted with the $15$ percent error of the contests' winners. A standard time series forecasting algorithm, the ARIMA model, yields a $31$ percent error on the average. However, since the expected error of a randomly selected forecaster is about $22$ percent, our conclusion is that selective attention is the most likely explanation for the mysterious high accuracy of the crowd reported in the literature.

physics.data-an↗

arXiv2022

The synergy between two threats: disinformation and Covid-19

The breakdown of trusted sources of information is probably one of the most serious problems today, since in the absence of a common ground, it will be impossible to address the problems that trouble our contemporary world. The Covid-19 pandemic is just a recent situation where the lack of agreed stances has led to failure and hopelessness. In fact, disinformation surrounding the Covid-19 has been a distinctive feature of this pandemic since its very beginning and has hampered what is perhaps the most important initiative to prevent the spread of the coronavirus, viz., an effective communication between scientifically-minded health authorities and the general public. To investigate how disinformation threatens epistemic security, here we propose and solve analytically an evolutionary game-theoretic model where the individuals must accurately estimate some property of their hazardous environment. They can either explore the environment or copy the estimate from another individual, who may display a distorted version of its estimate. We find that the exploration-only strategy is optimal when the environment is relatively safe and the individuals are not reliable. In this doomsday scenario, disinformation erodes trust and suppresses the ability of the individuals to share information with one another.

physics.soc-ph↗

arXiv2021

Long-term Scientific Impact Revisited

Citation based measures are widely used as quantitative proxies for subjective factors such as the importance of a paper or even the worth of individual researchers. Here we analyze the citation histories of $4669$ papers published in journals of the American Physical Society between $1960$ and $1968$ and argue that state-of-the-art models of citation dynamics and algorithms for forecasting nonstationary time series are very likely to fail to predict the long-term ($50$ years after publication) citation counts of highly-cited papers using citation data collected in a short period (say, $10$ years) after publication. This is so because those papers do not exhibit distinctive short-term citation patterns, although their long-term citation patterns clearly set them apart from the other papers. We conclude that even if one accepts that citation counts are proxies for the quality of papers, they are not useful evaluative tools since the short-term counts are not informative about the long-term counts in the case of highly-cited papers.

cs.DL↗

arXiv2021

A stochastic model for the influence of social distancing on loneliness

The short-term economic consequences of the critical measures employed to curb the transmission of Covid-19 are all too familiar, but the consequences of isolation and loneliness resulting from those measures on the mental well-being of the population and their ensuing long-term economic effects are largely unknown. Here we offer a stochastic agent-based model to investigate social restriction measures in a community where the feelings of loneliness of the agents dwindle when they are socializing and grow when they are alone. In addition, the intensity of those feelings, which are measured by a real variable that we term degree of loneliness, determines whether the agent will seek social contact or not. We find that decrease of the number, quality or duration of social contacts lead the community to enter a regime of burnout in which the degree of loneliness diverges, although the number of lonely agents at a given moment amounts to only a fraction of the total population. This regime of mental breakdown is separated from the healthy regime, where the degree of loneliness is finite, by a continuous phase transition. We show that the community dynamics is described extremely well by a simple mean-field theory so our conclusions can be easily verified for different scenarios and parameter settings. The appearance of the burnout regime illustrates neatly the side effects of social distancing, which give to many of us the choice between physical infection and mental breakdown.

physics.soc-ph↗

arXiv2021

Wisdom of crowds: much ado about nothing

The puzzling idea that the combination of independent estimates of the magnitude of a quantity results in a very accurate prediction, which is superior to any or, at least, to most of the individual estimates is known as the wisdom of crowds. Here we use the Federal Reserve Bank of Philadelphia's Survey of Professional Forecasters database to confront the statistical and psychophysical explanations of this phenomenon. Overall we find that the data do not support any of the proposed explanations of the wisdom of crowds. In particular, we find a positive correlation between the variance (or diversity) of the estimates and the crowd error in disagreement with some interpretations of the diversity prediction theorem. In addition, contra the predictions of the psychophysical augmented quincunx model, we find that the skew of the estimates offers no information about the crowd error. More importantly, we find that the crowd beats all individuals in less than 2% of the forecasts and beats most individuals in less than 70% of the forecasts, which means that there is a sporting chance that an individual selected at random will perform better than the crowd. These results contrast starkly with the performance of non-natural crowds composed of unbiased forecasters which beat most individuals in practically all forecasts. The moderate statistical advantage of a real-world crowd over its members does not justify the ado about its wisdom, which is most likely a product of the selective attention fallacy.

stat.AP↗

arXiv2020

The paradox of productivity during quarantine: an agent-based simulation

Economies across the globe were brought to their knees due to lockdowns and social restriction measures to contain the spread of the SARS-CoV-2, despite the quick switch to remote working. This downfall may be partially explained by the "water cooler effect", which holds that higher levels of social interaction lead to higher productivity due to a boost in people's mood. Somewhat paradoxically, however, there are reports of increased productivity in the remote working scenario. Here we address quantitatively this issue using a variety of experimental findings of social psychology that address the interplay between mood, social interaction and productivity to set forth an agent-based model for a workplace composed of extrovert and introvert agent stereotypes that differ solely on their propensities to initiate a social interaction. We find that the effects of curtailing social interactions depend on the proportion of the stereotypes in the working group: while the social restriction measures always have a negative impact on the productivity of groups composed predominantly of introverts, they may actually improve the productivity of groups composed predominantly of extroverts. Our results offer a proof of concept that the paradox of productivity during quarantine can be explained by taking into account the distinct effects of the social distancing measures on extroverts and introverts.

cs.MA↗

arXiv2020

A SIR epidemic model for citation dynamics

The study of citations in the scientific literature crosses the boundaries between the traditional branches of science and stands on its own as a most profitable research field dubbed the `science of science'. Although the understanding of the citation histories of individual papers involves many intangible factors, the basic assumption that citations beget citations can explain most features of the empirical citation patterns. Here we use the SIR epidemic model as a mechanistic model for the citation dynamics of well-cited papers published in selected journals of the American Physical Society. The estimated epidemiological parameters offer insight on unknown quantities as the size of the community that could cite a paper and its ultimate impact on that community. We find a good, though imperfect, agreement between the rank of the journals obtained using the epidemiological parameters and the impact factor rank.

cs.DL↗

arXiv2020

Prediction diversity and selective attention in the wisdom of crowds

The wisdom of crowds is the idea that the combination of independent estimates of the magnitude of some quantity yields a remarkably accurate prediction, which is always more accurate than the average individual estimate. In addition, it is largely believed that the accuracy of the crowd can be improved by increasing the diversity of the estimates. Here we report the results of three experiments to probe the current understanding of the wisdom of crowds, namely, the estimates of the number of candies in a jar, the length of a paper strip, and the number of pages of a book. We find that the collective estimate is better than the majority of the individual estimates in all three experiments. In disagreement with the prediction diversity theorem, we find no significant correlation between the prediction diversity and the collective error. The poor accuracy of the crowd on some experiments lead us to conjecture that its alleged accuracy is most likely an artifice of selective attention.

cs.IT↗

arXiv2020

The surprising little effectiveness of cooperative algorithms in parallel problem solving

Biological and cultural inspired optimization algorithms are nowadays part of the basic toolkit of a great many research domains. By mimicking processes in nature and animal societies, these general-purpose search algorithms promise to deliver optimal or near-optimal solutions using hardly any information on the optimization problems they are set to tackle. Here we study the performances of a cultural-inspired algorithm -- the imitative learning search -- as well as of asexual and sexual variants of evolutionary algorithms in finding the global maxima of NK-fitness landscapes. The main performance measure is the total number of agent updates required by the algorithms to find those global maxima and the baseline performance, which establishes the effectiveness of the cooperative algorithms, is set by the blind search in which the agents explore the problem space (binary strings) by flipping bits at random. We find that even for smooth landscapes that exhibit a single maximum, the evolutionary algorithms do not perform much better than the blind search due to the stochastic effects of the genetic roulette. The imitative learning is immune to this effect thanks to the deterministic choice of the fittest string in the population, which is used as a model for imitation. The tradeoff is that for rugged landscapes the imitative learning search is more prone to be trapped in local maxima than the evolutionary algorithms. In fact, in the case of rugged landscapes with a mild density of local maxima, the blind search either beats or matches the cooperative algorithms regardless of whether the task is to find the global maximum or to find the fittest state within a given runtime.

cs.MA↗

arXiv2019

Comfort-driven mobility produces spatial fragmentation in Axelrod's model

Axelrod's model for the dissemination of culture combines two key ingredients of social dynamics: social influence, through which people become more similar when they interact, and homophily, which is the tendency of individuals to interact preferentially with similar others. In Axelrod's model, the agents are fixed to the nodes of a network and are allowed to interact with a predetermined set of peers only, resulting in the frustration of a large number of agents that end up culturally isolated. Here we modify this model by allowing the agents to move away from their cultural opposites and stay put when near their cultural likes. The comfort, i.e., the tendency of an agent to stay put in a neighborhood, is determined by the cultural similarity with its neighbors. The less the comfort, the higher the odds that the agents will move apart a fixed step size. We find that the comfort-driven mobility fragments severely the influence network for low initial cultural diversity, resulting in a network composed of only microscopic components in the thermodynamic limit. For high initial cultural diversity and intermediate values of the step size, we find that a macroscopic component coexists with the microscopic ones. The transition between these two fragmentation regimes changes from continuous to discontinuous as the step size increases. In addition, we find that for both very small and very large step sizes the influence network is severely fragmented.

physics.soc-ph↗

arXiv2019

Policies for allocation of information in task-oriented groups: elitism and egalitarianism outperform welfarism

Communication or influence networks are probably the most controllable of all factors that are known to impact on the problem-solving capability of task-forces. In the case connections are costly, it is necessary to implement a policy to allocate them to the individuals. Here we use an agent-based model to study how distinct allocation policies affect the performance of a group of agents whose task is to find the global maxima of NK fitness landscapes. Agents cooperate by broadcasting messages informing on their fitness and use this information to imitate the fittest agent in their influence neighborhoods. The larger the influence neighborhood of an agent, the more links, and hence information, the agent receives. We find that the elitist policy in which agents with above-average fitness have their influence neighborhoods amplified, whereas agents with below-average fitness have theirs deflated, is optimal for smooth landscapes, provided the group size is not too small. For rugged landscapes, however, the elitist policy can perform very poorly for certain group sizes. In addition, we find that the egalitarian policy, in which the size of the influence neighborhood is the same for all agents, is optimal for both smooth and rugged landscapes in the case of small groups. The welfarist policy, in which the actions of the elitist policy are reversed, is always suboptimal, i.e., depending on the group size it is outperformed by either the elitist or the egalitarian policies.

cs.MA↗

arXiv2019

Mobility helps problem-solving systems to avoid Groupthink

Groupthink occurs when everyone in a group starts thinking alike, as when people put unlimited faith in a leader. Avoiding this phenomenon is a ubiquitous challenge to problem-solving enterprises and typical countermeasures involve the mobility of group members. Here we use an agent-based model of imitative learning to study the influence of the mobility of the agents on the time they require to find the global maxima of NK-fitness landscapes. The agents cooperate by exchanging information on their fitness and use this information to copy the fittest agent in their influence neighborhoods, which are determined by face-to-face interaction networks. The influence neighborhoods are variable since the agents perform random walks in a two-dimensional space. We find that mobility is slightly harmful for solving easy problems, i.e. problems that do not exhibit suboptimal solutions or local maxima. For difficult problems, however, mobility can prevent the imitative search being trapped in suboptimal solutions and guarantees a better performance than the independent search for any system size.

physics.soc-ph↗

arXiv2018

Influence of technological progress and renewability on the sustainability of ecosystem engineers populations

Overpopulation and environmental degradation due to inadequate resource-use are outcomes of human's ecosystem engineering that has profoundly modified the world's landscape. Despite the age-old concern that unchecked population and economic growth may be unsustainable, the prospect of societal collapse remains contentious today. Contrasting with the usual approach to modeling human-nature interactions, which are based on the Lotka-Volterra predator-prey model with humans as the predators and nature as the prey, here we address this issue using a discrete-time population dynamics model of ecosystem engineers. The growth of the population of engineers is modeled by the Beverton-Holt equation with a density-dependent carrying capacity that is proportional to the number of usable habitats. These habitats (e.g., farms) are the products of the work of the individuals on the virgin habitats (e.g., native forests), hence the denomination engineers of ecosystems to those agents. The human-made habitats decay into degraded habitats, which eventually regenerate into virgin habitats. For slow regeneration resources, we find that the dynamics is dominated by cycles of prosperity and collapse, in which the population reaches vanishing small densities. However, increase of the efficiency of the engineers to explore the resources eliminates the dangerous cyclical patterns of feast and famine and leads to a stable equilibrium that balances population growth and resource availability. This finding supports the viewpoint of growth optimists that technological progress may avoid collapse.

q-bio.PE↗

arXiv2018

Individual decision making in task-oriented groups

The strategies adopted by individuals to select relevant information to pass on are central to understanding problem solving by groups. Here we use agent-based simulations to revisit a cooperative problem-solving scenario where the task is to find the common card in decks distributed to the group members. The agents can display only a sample of their cards and we explore different strategies to select those samples based on the confidences assigned to the cards. An agent's confidence that a particular card is the correct one is given by the number of times it observed that card in the decks of the other agents. We use a Gibbs distribution to select the card samples with the temperature measuring the strength of a noise that prevents the agents to correctly rank the cards. The group is guaranteed to find the common card in all runs solely in the infinite temperature limit, where the cards are sampled regardless of their confidences. In this case, we obtain the scaling form of the time constant that characterizes the asymptotic exponential decay of the failure probability. For finite time, however, a finite temperature yields a probability of failure that is several orders of magnitude lower than in the infinite temperature limit. The available experimental results are consistent with the decision-making model for finite temperature only.

cs.MA↗

arXiv2018

Predictability of the imitative learning trajectories

The fitness landscape metaphor plays a central role on the modeling of optimizing principles in many research fields, ranging from evolutionary biology, where it was first introduced, to management research. Here we consider the ensemble of trajectories of the imitative learning search, in which agents exchange information on their fitness and imitate the fittest agent in the population aiming at reaching the global maximum of the fitness landscape. We assess the degree to which the starting and ending points determine the learning trajectories using two measures, namely, the predictability that yields the probability that two randomly chosen trajectories are the same, and the mean path divergence that gauges the dissimilarity between two learning trajectories. We find that the predictability is greater in rugged landscapes than in smooth ones. The mean path divergence, however, is strongly affected by the search parameters -- population size and imitation propensity -- that obliterate the influence of the underlying landscape. The learning trajectories become more deterministic, in the sense that there are fewer distinct trajectories and those trajectories are more similar to each other, with increasing population size and imitation propensity. In addition, we find that the roughness of the learning trajectories, which measures the deviation from additivity of the fitness function, is always greater than the roughness estimated over the entire fitness landscape.

q-bio.PE↗

arXiv2018

Agent-based models of collective intelligence

Collective or group intelligence is manifested in the fact that a team of cooperating agents can solve problems more efficiently than when those agents work in isolation. Although cooperation is, in general, a successful problem solving strategy, it is not clear whether it merely speeds up the time to find the solution, or whether it alters qualitatively the statistical signature of the search for the solution. Here we review and offer insights on two agent-based models of distributed cooperative problem-solving systems, whose task is to solve a cryptarithmetic puzzle. The first model is the imitative learning search in which the agents exchange information on the quality of their partial solutions to the puzzle and imitate the most successful agent in the group. This scenario predicts a very poor performance in the case imitation is too frequent or the group is too large, a phenomenon akin to Groupthink of social psychology. The second model is the blackboard organization in which agents read and post hints on a public blackboard. This brainstorming scenario performs the best when there is a stringent limit to the amount of information that is exhibited on the board. Both cooperative scenarios produce a substantial speed up of the time to solve the puzzle as compared with the situation where the agents work in isolation. The statistical signature of the search, however, is the same as that of the independent search.

cs.MA↗