BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

José F. Fontanari

José F. Fontanari contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
physics.soc-phDigital LibrariesMultiagent Systemsphysics.data-anPopulations and EvolutionBiological Physicscond-mat.stat-mechNeural and Evolutionary Computingnlin.AOQuantitative MethodsSocial and Information Networks

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author
9works
0followers
11topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

9 published item(s)

preprint2026arXiv

The Same Problem by Different Names: Unifying Regression Dilution and Regression to the Mean

Regression to the Mean and Regression Dilution are often viewed as unrelated issues in the clinical and ecological literatures. In reality, they are different names for the same problem: measurement error in an independent variable that biases the perceived relationship between two factors. This study unifies these traditions by comparing specialized clinical tools, like the Berry correction, with standard structural estimators such as Major Axis and Reduced Major Axis regression. Using an analytical framework, we evaluate how these methods perform across various noise levels and sample sizes. Our results show that the Berry method is a specialized tool designed for clinical scenarios where a 1:1 relationship is expected. However, applying it to ecological trade-offs with negative slopes can lead to severe errors. We provide maps of optimality to identify which estimator most accurately recovers the true biological signal under different conditions. By reconciling these disparate methods, we offer a principled guide for researchers to choose the correct tool based on their data's noise profile rather than their disciplinary tradition.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Non-equilibrium phase transition and cultural drift in the continuous-trait Axelrod model

The standard Axelrod model of cultural dissemination, based on discrete cultural traits, exhibits a non-equilibrium phase transition but is inherently limited by its inability to continuously probe the critical behavior. We address this limitation by introducing a generalized Axelrod model utilizing continuous cultural traits confined to the interval $[0,1]$, and a similarity threshold, $d$, that serves as a continuous control parameter representing cultural tolerance. This framework allows for a robust analysis of the model's critical properties and its dynamics under cultural drift (copying noise). For the perfect copying scenario, we precisely locate the critical threshold $d_c$, which separates the disordered (fragmented) and ordered (polarized) phases. Through Finite-Size Scaling, we find that the mean domain density vanishes continuously at $d_c$ with the exponent $β= 1/3$. Simultaneously, the largest domain fraction displays a surprising discontinuous jump at $d_c$. We find that the finite size effects in the critical region are governed by the exponent $ν=2$ for both the continuous and discontinuous transitions. Under imperfect copying, persistent noise introduces a powerful selective pressure on the trait space, leading to the emergence of two symmetry-related attractors at the trait values $d$ and $1-d$. However, these noise-induced attractors prove fragile in the thermodynamic limit, becoming unstable at large lattice sizes, which directly accounts for the observed failure of the dynamics to freeze under sustained cultural drift. This suggests that in large, continuously evolving societies, true cultural convergence is highly unlikely, leading instead to sustained fragmentation and nonstationary dynamics where cultural domains never fully stabilize.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

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.

0 citations0 reviewsNo code linkedOpen access