CoChat
Sign Up
Cited by 21

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Public Goods Games on Coevolving Social Network Models

Tomassini, Marco, Antonioni, Alberto·2020·Frontiers in Physics
DOI 10.3389/fphy.2020.00058
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/fphy.2020.00058

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Figures

FIGURE 1 Visual representation of public goods games played on a network structure of seven nodes. Each PGG is centered in a focal node i and it is represented by a circle Gi . For example, the node a has four direct neighbors ( b , c , d , f ) and it participates in five PGGs, i.e., the circle Ga centered in a including its four neighbors, and other four PGGs represented by circles centered in its neighbors: Gb , Gc , Gd , Gf . Note that a node may indirectly interact with other nodes that are not direct neighbors. Colors represent the node contribution into the common pools in which it participates.
FIGURE 1 Visual representation of public goods games played on a network structure of seven nodes. Each PGG is centered in a focal node i and it is represented by a circle Gi . For example, the node a has four direct neighbors ( b , c , d , f ) and it participates in five PGGs, i.e., the circle Ga centered in a including its four neighbors, and other four PGGs represented by circles centered in its neighbors: Gb , Gc , Gd , Gf . Note that a node may indirectly interact with other nodes that are not direct neighbors. Colors represent the node contribution into the common pools in which it participates.CC-BY · From the PDF
FIGURE 2 Average individual contribution as a function of round number for several values of the enhancement factor r as shown in the inset. The average is taken over 40 independent repetitions of 40 rounds on a network of size 500 and α = 0.3. (A) Static case, i.e., the network topology doesn't change during the simulation. (B) Coevolving network case, i.e., unsatisfied players may redirect a link to a random group with probability p = 0.5.
FIGURE 2 Average individual contribution as a function of round number for several values of the enhancement factor r as shown in the inset. The average is taken over 40 independent repetitions of 40 rounds on a network of size 500 and α = 0.3. (A) Static case, i.e., the network topology doesn't change during the simulation. (B) Coevolving network case, i.e., unsatisfied players may redirect a link to a random group with probability p = 0.5.CC-BY · From the PDF
FIGURE 3 Average individual contribution as a function of round number for several values of the enhancement factor r as shown in the inset. The average is taken over 40 independent repetitions of 40 rounds on an initial network of size 500 and α = 0.3. (A) The probability of rewiring when an agent is unsatisfied is p = 0.2; (B) p = 0.8.
FIGURE 3 Average individual contribution as a function of round number for several values of the enhancement factor r as shown in the inset. The average is taken over 40 independent repetitions of 40 rounds on an initial network of size 500 and α = 0.3. (A) The probability of rewiring when an agent is unsatisfied is p = 0.2; (B) p = 0.8.CC-BY · From the PDF
FIGURE 4 Degree distribution functions (DDFs) and graphical representation of the corresponding network structures. Node colors represent the node contribution to the common pools in which it participates: full contribution (blue), intermediate contribution (yellow), no contribution (red). Node size corresponds to the node degree. (A) DDF of an initial network of 500 nodes built according to the procedure described in Antonioni and Tomassini [34]. (B) DDF of the evolved network after 40 rounds of play with a link redirection probability p = 0.5 and an enhancement factor r = 1.2. (C) DDF of the final evolved network after 40 rounds of play having rewiring probability p = 0.5 and enhancement factor r = 2.4. Note the different scales in DDFs and figure zooms.
FIGURE 4 Degree distribution functions (DDFs) and graphical representation of the corresponding network structures. Node colors represent the node contribution to the common pools in which it participates: full contribution (blue), intermediate contribution (yellow), no contribution (red). Node size corresponds to the node degree. (A) DDF of an initial network of 500 nodes built according to the procedure described in Antonioni and Tomassini [34]. (B) DDF of the evolved network after 40 rounds of play with a link redirection probability p = 0.5 and an enhancement factor r = 1.2. (C) DDF of the final evolved network after 40 rounds of play having rewiring probability p = 0.5 and enhancement factor r = 2.4. Note the different scales in DDFs and figure zooms.CC-BY · From the PDF
FIGURE 5 Evolution of the average clustering coefficient during a run of 40 rounds for the same r values as in Table 1 . Color codes: r = 1.2, red; r = 1.8, green; r = 2.0, yellow; r = 2.2, blue; r = 2.4, black.
FIGURE 5 Evolution of the average clustering coefficient during a run of 40 rounds for the same r values as in Table 1 . Color codes: r = 1.2, red; r = 1.8, green; r = 2.0, yellow; r = 2.2, blue; r = 2.4, black.CC-BY · From the PDF
FIGURE 6 Wikipedia 'who votes on whom' network (see text for details). Average individual contribution as a function of round number for several values of the enhancement factor r as shown in the inset. The average is taken over 40 independent repetitions of 40 rounds on the Wikipedia network using (A) p = 0 (static case); (B) p = 0.5 (coevolving).
FIGURE 6 Wikipedia 'who votes on whom' network (see text for details). Average individual contribution as a function of round number for several values of the enhancement factor r as shown in the inset. The average is taken over 40 independent repetitions of 40 rounds on the Wikipedia network using (A) p = 0 (static case); (B) p = 0.5 (coevolving).CC-BY · From the PDF

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 5 references

Research works printed but not linked: 39

  1. Barrett, 2007, Why Cooperate?, 10.1093/acprof:oso/9780199211890.001.0001 · DOI
  2. Hardin, 1968, The tragedy of the commons, Science., 162, 1243, 10.1126/science.162.3859.1243 · DOI · Paper page
  3. Andreoni, 1995, Cooperation in public-goods experiments: kindness or confusion?, Am Econ Rev., 85, 891
  4. Fehr, 2002, Altruistic punishment in humans, Nature., 415, 137, 10.1038/415137a · DOI · Paper page
  5. Burton-Chellew, 2013, Prosocial preferences do not explain human cooperation in public-goods games, Proc Natl Acad Sci USA., 110, 216, 10.1073/pnas.1210960110 · DOI
  6. Burton-Chellew, 2016, Conditional cooperation and confusion in public-goods experiments, Proc Natl Acad Sci USA., 113, 1291, 10.1073/pnas.1509740113 · DOI
  7. Burton-Chellew, 2018, Payoff-based learning explains the decline in cooperation in public goods games, Proc R Soc Lond B Biol Sci., 282, 20142678, 10.1098/rspb.2014.2678 · DOI · Paper page
  8. Li, 2018, Punishment diminishes the benefits of network reciprocity in social dilemma experiments, Proc Natl Acad Sci USA., 115, 30, 10.1073/pnas.1707505115 · DOI
  9. Wang, 2018, Exploiting a cognitive bias promotes cooperation in social dilemma experiments, Nat Commun., 9, 1, 10.1038/s41467-018-05259-5 · DOI
  10. Antonioni, 2014, Know thy neighbor: costly information can hurt cooperation in dynamic networks, PLoS ONE., 9, e110788, 10.1371/journal.pone.0110788 · DOI
  11. Cuesta, 2014, Reputation drives cooperative behaviour and network formation in human groups, Sci Rep., 5, 7843, 10.1038/srep07843 · DOI
  12. Barcelo, 2015, Group size effect on cooperation in one-shot social dilemmas, Sci Rep., 5, 1, 10.1038/srep07937 · DOI
  13. Capraro, 2015, Group size effect on cooperation in one-shot social dilemmas II: curvilinear effect, PLoS ONE., 10, e0131419, 10.1371/journal.pone.0131419 · DOI
  14. Antonioni, 2016, Cooperation survives and cheating pays in a dynamic network structure with unreliable reputation, Sci Rep., 6, 27160, 10.1038/srep27160 · DOI
  15. Lozano, 2018, Cooperation on dynamic networks within an uncertain reputation environment, Sci Rep., 8, 9093, 10.1038/s41598-018-27544-5 · DOI
  16. Pereda, 2019, Large scale and information effects on cooperation in public good games, Sci Rep., 9, 15023, 10.1038/s41598-019-50964-w · DOI
  17. Santos, 2008, Social diversity promotes the emergence of cooperation in public goods games, Nature., 454, 213, 10.1038/nature06940 · DOI
  18. Gómez-Gardeñes, 2011, Disentangling social and group heterogeneities: public goods games on complex networks, Europhys Lett., 95, 68003, 10.1209/0295-5075/95/68003 · DOI
  19. Gómez-Gardeñes, 2011, Evolutionary games defined at the network mesoscale: the public goods game, Chaos., 21, 016113, 10.1063/1.3535579 · DOI
  20. Javarone, 2016, Conformity-driven agents support ordered phases in the spatial public goods game, Europhys Lett., 114, 38001, 10.1209/0295-5075/114/38001 · DOI
  21. Perc, 2013, Evolutionary dynamics of group interactions on structured populations: a review, J R Soc Interface., 10, 20120997, 10.1098/rsif.2012.0997 · DOI · Paper page
  22. Perc, 2017, Statistical physics of human cooperation, Phys Rep., 687, 1, 10.1016/j.physrep.2017.05.004 · DOI
  23. Tomassini, 2019, Computational behavioral models for public goods games on social networks, Games., 10, 35, 10.3390/g10030035 · DOI
  24. Eguíluz, 2005, Cooperation and the emergence of role differentiation in the dynamics of social networks, Am J Sociol., 110, 977, 10.1086/428716 · DOI
  25. Pestelacci, 2008, Evolution of cooperation and coordination in a dynamically networked society, J Biol Theory., 3, 139, 10.1162/biot.2008.3.2.139 · DOI
  26. Perc, 2010, Coevolutionary games–a mini review, Biosystems., 99, 109, 10.1016/j.biosystems.2009.10.003 · DOI
  27. Rand, 2011, Dynamic social networks promote cooperation in experiments with humans, Proc Natl Acad Sci USA., 108, 19193, 10.1073/pnas.1108243108 · DOI · Paper page
  28. Gallo, 2015, The effects of reputational and social knowledge on cooperation, Proc Natl Acad Sci USA., 112, 3647, 10.1073/pnas.1415883112 · DOI
  29. Antonioni, 2017, A growing social network model in geographical space, J Stat Mech., 2017, 093403, 10.1088/1742-5468/aa819c · DOI
  30. Newman, 2018, Networks: An Introduction, 10.1093/oso/9780198805090.001.0001 · DOI
  31. Pereda, 2019, Group size effects and critical mass in public goods games, Sci Rep., 9, 5503, 10.1038/s41598-019-41988-3 · DOI
  32. Smaldino, 2011, An institutional mechanism for assortment in an ecology of games, PLoS ONE., 6, e23019, 10.1371/journal.pone.0023019 · DOI
  33. Peña, 2012, Bipartite graphs as models of population structures in evolutionary multiplayer games, PLoS ONE., 7, e44514, 10.1371/journal.pone.0044514 · DOI
  34. Wu, 2009, Individual's expulsion to nasty environment promotes cooperation in public goods games, Europhys Lett., 88, 30011, 10.1209/0295-5075/88/30011 · DOI
  35. Zhang, 2011, Coevolving agent strategies and network topology for the public goods games, Eur Phys J B., 80, 217, 10.1140/epjb/e2011-10470-2 · DOI
  36. Zhang, 2011, Aspiration-induced reconnection in spatial public-goods game, Europhys Lett., 94, 18006, 10.1209/0295-5075/94/18006 · DOI
  37. Li, 2013, The coevolution of partner switching and strategy updating in non-excludable public goods game, Phys A Stat Mech Appl., 392, 4956, 10.1016/j.physa.2013.05.054 · DOI
  38. Li, 2015, Network topology control strategy based on spatial evolutionary public goods game, Phys A Stat Mech Appl., 432, 16, 10.1016/j.physa.2015.02.101 · DOI
  39. Rossi, 2015, The network data repository with interactive graph analytics and visualization, 10.1609/aaai.v29i1.9277 · DOI

Cited by

Sorted by citations

See all 21

Metadata

Authors
Tomassini, M., & Antonioni, A.
Venue
Frontiers in Physics
Publisher
Frontiers Media SA
Published
2020-03-11
Volume
8
ISSN
2296-424X
DOI
10.3389/fphy.2020.00058
Cited by
21
References
5
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • Ministerio de Economía, Industria y Competitividad, Gobierno de España
  • Ministerio de Ciencia, Innovación y Universidades

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Public Goods Games on Coevolving Social Network Models. https://app.cochat.ai/papers/10.3389/fphy.2020.00058
The paper
Tomassini, M., & Antonioni, A. (2020). Public Goods Games on Coevolving Social Network Models. Frontiers in Physics. https://doi.org/10.3389/fphy.2020.00058