pith. machine review for the scientific record. sign in

arxiv: 2605.13750 · v1 · submitted 2026-05-13 · ⚛️ physics.soc-ph · cs.GT· q-bio.PE

Recognition: no theorem link

The Co-evolution of Costly Signaling and Cooperation in Social Dilemmas

Mahdi Abolhasani , Saman Moghimi-Araghi , Mohammad Salahshour
Authors on Pith no claims yet

Pith reviewed 2026-05-14 17:31 UTC · model grok-4.3

classification ⚛️ physics.soc-ph cs.GTq-bio.PE
keywords costly signalingcooperationsocial dilemmasevolutionary gamesprisoner's dilemmastag huntsnowdrift gamespatial structure
0
0 comments X

The pith

Costly signals persist in social dilemmas because they organize cooperative responses rather than through their raw production costs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines agents that evolve both to emit costly signals and to base their cooperation or defection on the signals they observe from others. Across the Prisoner's Dilemma, Snowdrift game, and Stag Hunt, simulations demonstrate that signals are retained when they currently draw cooperative actions from receivers, creating a feedback that favors both the signal and the response. In well-mixed populations this produces partial cooperation in the first two games and near-complete cooperation in the third, while spatial lattices increase cooperation through local clustering. A reduced analysis shows that simple average feedback already accounts for the Snowdrift and Stag Hunt outcomes, yet the Prisoner's Dilemma needs extra transient effects from rare signals or inheritance to explain its behavior. The central result is that signaling and cooperation can reinforce each other by reshaping the effective game environment without requiring the signal to carry fixed information.

Core claim

Signals are selected less by their raw production costs than by the cooperative responses they currently elicit. In well-mixed populations, the mechanism sustains partial cooperation in PD and SD and drives near-complete cooperation in SH. On lattices, cooperation is strengthened further by local assortment. A reduced mean-field analysis explains why average population feedback is already sufficient in SD and SH, but not in the PD. To account for the PD dynamics, the reduced theory must include transient correlations associated with rare signals, inheritance, or spatial clustering.

What carries the argument

The coevolutionary loop in which agents emit costly signals and condition their game actions on the signals they observe, allowing signals to persist through the cooperative responses they elicit.

If this is right

  • Well-mixed populations reach partial cooperation in the Prisoner's Dilemma and Snowdrift game and near-complete cooperation in the Stag Hunt.
  • Local assortment on lattices raises cooperation levels above the well-mixed case in all three games.
  • Average population feedback alone suffices to sustain the outcomes in the Snowdrift and Stag Hunt games.
  • The Prisoner's Dilemma requires transient correlations from rare signals, inheritance, or clustering to explain its sustained cooperation.
  • Costly signals endure because they transiently reshape the effective strategic environment faced by the population.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If observation of signals becomes noisy or costly, the feedback loop should break and cooperation should collapse toward the levels seen without signaling.
  • The same response-elicitation logic could organize behavior in human settings where costly displays coordinate actions without being strictly honest indicators.
  • Introducing payoff fluctuations that switch between the three games would test whether the mechanism remains stable when the underlying dilemma itself changes over time.
  • Allowing agents to emit multiple distinct signals at once might show whether added signaling complexity helps or hinders the organization of cooperation.

Load-bearing premise

Agents can reliably observe signals and then choose their cooperation or defection based on the signals they see.

What would settle it

Run the same evolutionary simulations but remove the ability of agents to condition their game move on observed signals; if cooperation levels fall to the no-signaling baseline in all three games, the mechanism is falsified.

Figures

Figures reproduced from arXiv: 2605.13750 by Mahdi Abolhasani, Mohammad Salahshour, Saman Moghimi-Araghi.

Figure 1
Figure 1. Figure 1: Well-mixed action dynamics across the three canonical games. Panels (a,b) show the [PITH_FULL_IMAGE:figures/full_fig_p017_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Well-mixed signal statistics across the three canonical games. Panels (a,b) show the [PITH_FULL_IMAGE:figures/full_fig_p018_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Structured-population action and outcome statistics across the three canonical games. [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Structured-population signal statistics across the three canonical games. Panels (a,b) [PITH_FULL_IMAGE:figures/full_fig_p020_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Adaptation in fluctuating strategic environments across population structures. The [PITH_FULL_IMAGE:figures/full_fig_p021_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Stochastic numerical solution of the independence closure across the three canonical [PITH_FULL_IMAGE:figures/full_fig_p022_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Signal statistics for the stochastic numerical solution of the independence closure. The [PITH_FULL_IMAGE:figures/full_fig_p023_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Stochastic numerical solution of the rare-signal-protection closure across the three [PITH_FULL_IMAGE:figures/full_fig_p024_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Signal statistics for the stochastic numerical solution of the rare-signal-protection [PITH_FULL_IMAGE:figures/full_fig_p025_9.png] view at source ↗
read the original abstract

Costly cooperation and costly signaling are both difficult to reconcile with simple fitness maximization, yet both are common in biological and social systems. We study a model in which agents emit costly signals and condition their actions on the signals they observe. Across the Prisoner's Dilemma (PD), Snowdrift (SD), and Stag Hunt (SH) games, we ask when this coevolutionary process can sustain cooperation and how it changes across well-mixed populations, spatial lattices, and fluctuating strategic environments. The simulations show that signals are selected less by their raw production costs than by the cooperative responses they currently elicit. In well-mixed populations, the mechanism sustains partial cooperation in PD and SD and drives near-complete cooperation in SH. On lattices, cooperation is strengthened further by local assortment. A reduced mean-field analysis explains why average population feedback is already sufficient in SD and SH, but not in the PD. To account for the PD dynamics, the reduced theory must include transient correlations associated with rare signals, inheritance, or spatial clustering. Our results therefore delineate a class of settings in which costly signals persist because they transiently organize cooperative responses and thereby reshape the effective strategic environment.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The manuscript develops an agent-based evolutionary model in which agents produce costly signals and condition their actions in social dilemma games (Prisoner's Dilemma, Snowdrift, Stag Hunt) on the signals they observe from others. Simulations in well-mixed populations and on lattices, supplemented by a reduced mean-field analysis, demonstrate that signal selection is driven primarily by the cooperative responses elicited rather than by production costs alone. This mechanism sustains partial cooperation in PD and SD, near-complete in SH in well-mixed settings, with spatial structure enhancing cooperation further. The analysis highlights the role of transient correlations in PD dynamics.

Significance. If the results hold, this provides a valuable contribution to the literature on the evolution of cooperation and signaling by delineating conditions under which costly signals persist due to their role in organizing cooperative responses. The explicit comparison across game types and population structures, along with the mean-field reduction that explains why average feedback suffices in some games but requires additional terms in PD, strengthens the work. The use of forward simulations of an explicit process is a strength, avoiding circularity in the derivations.

minor comments (3)
  1. Abstract: The abstract would benefit from briefly stating the ranges or specific values of key parameters (e.g., signal production costs, benefit-to-cost ratios, mutation rates) used in the simulations, as their absence makes it difficult to assess the robustness of the reported cooperation levels without consulting the main text.
  2. Simulation results section: Error bars or standard deviations across independent runs are not mentioned in the description of the cooperation levels; including these would strengthen the presentation of the quantitative outcomes for PD, SD, and SH.
  3. Mean-field analysis: The reduced model is described as explanatory, but a short appendix or subsection explicitly listing the assumptions and the precise form of the transient correlation terms added for the PD case would improve clarity for readers attempting to reproduce the analysis.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive and accurate summary of our manuscript, which correctly captures the core mechanism by which costly signals organize cooperative responses across the PD, SD, and SH games. We appreciate the recommendation for minor revision and the recognition of the value added by the explicit comparisons across game types, population structures, and the mean-field reduction.

Circularity Check

0 steps flagged

No significant circularity; results from explicit forward simulation

full rationale

The paper derives its claims from agent-based evolutionary simulations of strategy updates driven by explicit payoffs (signaling costs plus game outcomes) across PD, SD, and SH, with a separate reduced mean-field analysis offered only as post-hoc explanation for average feedback effects. No equation or result is obtained by fitting a parameter to data and then relabeling the fit as a prediction, nor does any load-bearing step reduce to a self-citation or definitional equivalence. The reported distinction between cost-driven and response-driven signal selection is obtained by direct comparison of simulation trajectories, and the PD exception is handled by adding explicit mechanisms (spatial clustering, inheritance) whose effects are measured rather than assumed. The derivation chain is therefore self-contained against the simulation protocol.

Axiom & Free-Parameter Ledger

3 free parameters · 2 axioms · 0 invented entities

The model rests on standard evolutionary game theory assumptions plus the novel stipulation that signals are both costly and observable for conditioning. No new physical entities are introduced.

free parameters (3)
  • signal production cost
    Positive cost parameter required for the costly-signaling premise; exact value not stated in abstract.
  • benefit-to-cost ratio in each game
    Standard payoff parameters that determine the dilemma type (PD, SD, SH).
  • mutation rate and selection intensity
    Control the speed of evolutionary change in the agent-based simulations.
axioms (2)
  • domain assumption Agents update strategies proportionally to fitness derived from game payoffs minus signaling costs.
    Core assumption of evolutionary game dynamics invoked throughout the simulation description.
  • domain assumption Signals are perfectly observable and agents can condition actions on observed signal values.
    Required for the co-evolutionary mechanism; stated implicitly by the model setup.

pith-pipeline@v0.9.0 · 5517 in / 1393 out tokens · 48211 ms · 2026-05-14T17:31:39.662864+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

52 extracted references · 52 canonical work pages

  1. [1]

    Hamilton, W. D. (1964). The genetical evolution of social behaviour. II.Journal of Theo- retical Biology, 7(1), 17–52

    work page 1964

  2. [2]

    Trivers, R. L. (1971). The evolution of reciprocal altruism.Quarterly Review of Biology, 46(1), 35–57

    work page 1971

  3. [3]

    Axelrod, R., & Hamilton, W. D. (1981). The evolution of cooperation.Science, 211(4489), 1390–1396

    work page 1981

  4. [4]

    Nowak, M. A. (2006). Five rules for the evolution of cooperation.Science, 314(5805), 1560– 1563

    work page 2006

  5. [5]

    A., & Nowak, M

    Imhof, L. A., & Nowak, M. A. (2006). Evolutionary game dynamics in a Wright–Fisher process.Journal of Mathematical Biology, 52, 667–681

    work page 2006

  6. [6]

    Lehmann, L., & Keller, L. (2006). The evolution of cooperation and altruism: A general framework and a classification of models.Journal of Evolutionary Biology, 19(5), 1365– 1376

    work page 2006

  7. [7]

    G., & Nowak, M

    Rand, D. G., & Nowak, M. A. (2013). Human cooperation.Trends in Cognitive Sciences, 17(8), 413–425

    work page 2013

  8. [8]

    Zahavi, A. (1975). Mate selection—a selection for a handicap.Journal of Theoretical Biol- ogy, 53(1), 205–214

    work page 1975

  9. [9]

    Grafen, A. (1990). Biological signals as handicaps.Journal of Theoretical Biology, 144(4), 517–546

    work page 1990

  10. [10]

    A., & Sigmund, K

    Nowak, M. A., & Sigmund, K. (1998). Evolution of indirect reciprocity by image scoring. Nature, 393, 573–577

    work page 1998

  11. [11]

    A., & Sigmund, K

    Nowak, M. A., & Sigmund, K. (2005). Evolution of indirect reciprocity.Nature, 437, 1291– 1298

    work page 2005

  12. [12]

    Ohtsuki, H., & Iwasa, Y. (2004). How should we define goodness? Reputation dynamics in indirect reciprocity.Journal of Theoretical Biology, 231(1), 107–120

    work page 2004

  13. [13]

    Panchanathan, K., & Boyd, R. (2003). A tale of two defectors: the importance of standing for evolution of indirect reciprocity.Journal of Theoretical Biology, 224(1), 115–126

    work page 2003

  14. [14]

    Salahshour, M. (2022). Interaction between games give rise to the evolution of moral norms of cooperation.PLoS Computational Biology, 18(9), e1010429

    work page 2022

  15. [15]

    A., & May, R

    Nowak, M. A., & May, R. M. (1992). Evolutionary games and spatial chaos.Nature, 359, 826–829

    work page 1992

  16. [16]

    Ohtsuki, H., & Nowak, M. A. (2006). The replicator equation on graphs.Journal of Theo- retical Biology, 243(1), 86–97. 14

    work page 2006

  17. [17]

    Traulsen, A., & Nowak, M. A. (2006). Evolution of cooperation by multilevel selection. Proceedings of the National Academy of Sciences of the United States of America, 103(29), 10952–10955

    work page 2006

  18. [18]

    Fehr, E., & G¨ achter, S. (2002). Altruistic punishment in humans.Nature, 415, 137–140

    work page 2002

  19. [19]

    Salahshour, M. (2021). Evolution of prosocial punishment in unstructured and structured populations and in the presence of antisocial punishment.PLoS ONE, 16(8), e0254860

    work page 2021

  20. [20]

    Cressman, R., Song, J.-W., Zhang, B.-Y., & Tao, Y. (2012). Cooperation and evolutionary dynamics in the public goods game with institutional incentives.Journal of Theoretical Biology, 299, 144–151

    work page 2012

  21. [21]

    Salahshour, M. (2020). Coevolution of cooperation and language.Physical Review E, 102(4), 042409

    work page 2020

  22. [22]

    L., Cohen, M

    Riolo, R. L., Cohen, M. D., & Axelrod, R. (2001). Evolution of cooperation without reci- procity.Nature, 414, 441–443

    work page 2001

  23. [23]

    Jansen, V. A. A., & van Baalen, M. (2006). Altruism through beard chromodynamics. Nature, 440, 663–666

    work page 2006

  24. [24]

    McElreath, R., Boyd, R., & Richerson, P. J. (2003). Shared norms and the evolution of ethnic markers.Current Anthropology, 44(1), 122–129

    work page 2003

  25. [25]

    Efferson, C., Lalive, R., & Fehr, E. (2008). The coevolution of cultural groups and ingroup favoritism.Science, 321(5897), 1844–1849

    work page 2008

  26. [26]

    Chen, Y., & Li, S. X. (2009). Group identity and social preferences.American Economic Review, 99(1), 431–457

    work page 2009

  27. [27]

    Hauert, C., De Monte, S., Hofbauer, J., & Sigmund, K. (2002). Volunteering as Red Queen mechanism for cooperation in public goods games.Science, 296(5570), 1129–1132

    work page 2002

  28. [28]

    Song, Z., & Han, T. A. (2025). Emergence of cooperation and commitment in optional Prisoner’s Dilemma.Applied Mathematical Modelling, In press, Article 116603

    work page 2025

  29. [29]

    No¨ e, R., & Hammerstein, P. (1994). Biological markets: Supply and demand determine the effect of partner choice in cooperation, mutualism and mating.Behavioral Ecology and Sociobiology, 35(1), 1–11

    work page 1994

  30. [30]

    Barclay, P., & Willer, R. (2007). Partner choice creates competitive altruism in humans. Proceedings of the Royal Society B: Biological Sciences, 274(1610), 749–753

    work page 2007

  31. [31]

    A., & Wang, L

    Fu, F., Hauert, C., Nowak, M. A., & Wang, L. (2008). Reputation-based partner choice promotes cooperation in social networks.Physical Review E, 78(2), 026117

    work page 2008

  32. [32]

    Salahshour, M. (2021). Freedom to choose between public resources promotes cooperation. PLoS Computational Biology, 17(2), e1008703

    work page 2021

  33. [33]

    Salahshour, M. (2021). Evolution of cooperation in costly institutions exhibits Red Queen and Black Queen dynamics in heterogeneous public goods.Communications Biology, 4, 1340

    work page 2021

  34. [34]

    Salahshour, M. (2021). Evolution of cooperation and consistent personalities in public goods games.Scientific Reports, 11, 23708. 15

    work page 2021

  35. [35]

    Salahshour, M. (2023). Evolution as a result of resource flow in ecosystems: Ecological dynamics can drive evolution.PLoS ONE, 18(10), e0286922

    work page 2023

  36. [36]

    Salahshour, M. (2025). Perceptual rationality: an evolutionary game theory of perceptually rational decision-making.Royal Society Open Science, 12(10), 251125

    work page 2025

  37. [37]

    Salahshour, M., & Couzin, I. D. (2025). Evolution of altruistic rationality provides a solu- tion to social dilemmas via rational reciprocity.Physical Review Research, 7(3), 033211

    work page 2025

  38. [38]

    A., & Grafen, A

    Johnstone, R. A., & Grafen, A. (1993). Dishonesty and the handicap principle.Animal Behaviour, 46(4), 759–764

    work page 1993

  39. [39]

    Lachmann, M., Sz´ amado, S., & Bergstrom, C. T. (2001). Cost and conflict in animal signals and human language.Proceedings of the National Academy of Sciences of the United States of America, 98(23), 13189–13194

    work page 2001

  40. [40]

    Sz´ amado, S. (2011). The cost of honesty and the fallacy of the handicap principle.Animal Behaviour, 81(1), 3–10

    work page 2011

  41. [41]

    Sz´ amado, S., Zachar, I., Cz´ egel, D., et al. (2023). Honesty in signalling games is maintained by trade-offs rather than costs.BMC Biology, 21, 4

    work page 2023

  42. [42]

    A., & Bowles, S

    Gintis, H., Smith, E. A., & Bowles, S. (2001). Costly signaling and cooperation.Journal of Theoretical Biology, 213(1), 103–119

    work page 2001

  43. [43]

    Roberts, G. (1998). Competitive altruism: From reciprocity to the handicap principle. Proceedings of the Royal Society B: Biological Sciences, 265(1394), 427–431

    work page 1998

  44. [44]

    Fehrler, S., & Przepiorka, W. (2013). Charitable giving as a signal of trustworthiness: Disentangling the signaling benefits of altruistic acts.Evolution and Human Behavior, 34(2), 139–145

    work page 2013

  45. [45]

    J., & Smith, S

    Raihani, N. J., & Smith, S. (2015). Competitive helping in online giving.Current Biology, 25(9), 1183–1186

    work page 2015

  46. [46]

    Dawkins, R., & Krebs, J. R. (1978). Animal signals: Information or manipulation? In J. R. Krebs & N. B. Davies (Eds.),Behavioural ecology: An evolutionary approach(pp. 282–309). Blackwell Scientific

    work page 1978

  47. [47]

    Salahshour, M. (2019). Evolution of costly signaling and partial cooperation.Scientific Reports, 9, 8792

    work page 2019

  48. [48]

    M., Stephens, P

    McNamara, J. M., Stephens, P. A., Dall, S. R. X., & Houston, A. I. (2009). Evolution of trust and trustworthiness: social awareness favours personality differences.Proceedings of the Royal Society B: Biological Sciences, 276(1657), 605–613

    work page 2009

  49. [49]

    Baker, C. C. M., Dall, S. R. X., & Rankin, D. J. (2012). Kin selection and the evolution of social information use in animal conflict.PLOS ONE, 7(2), e31664

    work page 2012

  50. [50]

    P latkowski, T. (2017). On derivation and evolutionary classification of social dilemma games.Dynamic Games and Applications, 7, 67–75

    work page 2017

  51. [51]

    Szab´ o, G., & F´ ath, G. (2007). Evolutionary games on graphs.Physics Reports, 446(4–6), 97–216

    work page 2007

  52. [52]

    Hauert, C., & Doebeli, M. (2004). Spatial structure often inhibits the evolution of cooper- ation in the snowdrift game.Nature, 428(6983), 643–646. 16 Figure 1: Well-mixed action dynamics across the three canonical games. Panels (a,b) show the Prisoner’s Dilemma, panels (c,d) the Snowdrift game, and panels (e,f) the Stag Hunt. Panels (a,c,e) report the fr...

    work page 2004