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REVIEW 4 major objections 5 minor 42 references

Agent-based dynamics of criminal propensity

T0 review · 4 major / 5 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read Formalising a criminological theory predicts that populations sharing a neutral view of their environment split into rule-abiding and rule-breaking extremes, while a shared non-neutral view leads to consensus.

desk verdict A genuine formalisation of a verbal criminology theory with some real analysis, but the headline neutral-perception polarisation result is a thin artefact of the shock threshold and the simulation evidence is too visual to carry the 'dominant oscillations' claim. read the letter →

arxiv 2607.29546 v1 pith:LHC7UDWU submitted 2026-07-31 nlin.AO

Daniel Holland , Abigail Saynor , Evelyn Svingen , Galane Luo This is my paper · ORCID
classification nlin.AO MSC 91D1037N99
keywords criminalpropensityboundedconfidenceagent-basedmodelopiniondynamicspolarisationoscillationsretributionandreciprocityperceptionofenvironment
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper formalises the Retribution and Reciprocity Model of crime causation as an agent-based dynamical system, called mRRM, and asks what population-level patterns the posited mechanisms can generate. Its central claims are that criminal propensity is dominated by persistent oscillations rather than convergence, and that a population sharing a neutral perception of its environment tends to polarise to the extremes, while a shared non-neutral perception produces consensus. The authors prove individual propensities stay bounded, give sufficient conditions for convergence to extremes or to one's perception of the environment, and derive a necessary condition for oscillations. A sympathetic reader would care because this is a rare case of a verbal criminological theory being made precise enough to generate novel, testable predictions about when offending tendencies fluctuate and when they split.

What carries the argument

The central object is a bounded-confidence opinion dynamics model extended with two features: an agent-specific attractor, the perception of the environment (PoE) P_i, and third-party witnesses whose propensities also update. The decisive mechanism is the threshold condition r|C_j − P_i| > |P_i| (and its witness analogue), which decides whether an interaction 'shocks' an agent into moving toward the interaction partner or lets the agent drift back toward P_i. When P_i = 0, every nonzero interaction is shocking, which makes neutral perceivers maximally exposed to influence and drives convergence to the absorbing extremes ±1; this is the mechanism behind the polarisation claim.

What would settle it

Re-run the simulations with the shock condition changed to r|C_j − P_i| > c for a fixed c > 0 (e.g., c = 0.1) and otherwise identical parameters; if a uniform neutral perception P = 0 still produces polarisation to ±1 for c well above 0, the central finding is contradicted. Alternatively, an empirical longitudinal study of a population with genuinely neutral perceptions of their environment should show a bimodal split in offending propensity if the model is right.

Watch

Extended reading notes

Core claim

The core discovery is that the two RRM mechanisms—reciprocal and retributive updating from interactions and witnessing, plus a drift toward each agent's fixed perception of the environment—are sufficient to produce sustained non-convergence and polarisation with no external driver. In simulations, most agents' criminal propensities oscillate persistently around their perception of the environment; when everyone shares the same perception, the population converges to that value unless the shared perception is near zero, in which case the population splits into two factions converging to the extremes. The authors show the neutral-polarisation effect follows directly from the shock threshold in

Load-bearing premise

The prediction that neutral perceivers polarise depends on the modelling choice that an interaction shocks an agent whenever r|C_j − P_i| exceeds |P_i|, so that at P_i = 0 every interaction is shocking; a different threshold with a positive constant would likely destroy the effect.

Editorial extensions

If this is right

  • If correct, persistent oscillations of criminal propensity arise from deterministic rules without adaptive confidence bounds, offering a mechanistic account of fluctuating offending tendencies.
  • A population sharing a non-neutral view of how it is treated will tend toward consensus in criminal propensity; a population sharing a neutral view will tend to split into rule-abiding (C=1) and rule-breaking (C=−1) factions.
  • The polarisation is confined to a narrow band of near-neutral perceptions whose width shrinks with population size and grows with retributive and reciprocal strength, so small changes in the shared perception near zero can flip a population between consensus and division.
  • The model traces propensity dynamics rather than generating crime rates; its predictions are qualitative regime claims, not calibrated quantitative forecasts.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension: the neutral-polarisation prediction hinges on the threshold's zero baseline; modifying the threshold to include a positive constant would likely erase the effect, so empirical studies of populations with genuinely neutral environmental perceptions are the natural check.
  • The individual-level oscillations may connect to RRM's population-level free-rider cycles only if perceptions are allowed to evolve; the paper itself flags this as future work, so linking the two timescales is a plausible next step.
  • The paper's self-averaging explanation for why larger populations polarise less suggests a network-structure experiment: hubs that concentrate shocking interactions could counteract averaging and preserve polarisation in large populations.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper formalises the verbal Retribution and Reciprocity Model (RRM) of criminology as an agent-based system of criminal propensity. Agents update their propensity C_i(t) in [-1,1] through pairwise interactions and third-party witnessing, combining a bounded-confidence-style influence term with drift towards a fixed perception-of-environment P_i. The analytical results include boundedness of propensities, sufficient conditions for convergence to the extremes or to P_i, and a necessary condition for oscillatory dynamics. Simulations with 100 agents are used to claim that persistent oscillations are the dominant regime and that a uniform neutral perception of the environment produces polarisation to the extremes while a uniform non-neutral perception produces consensus, with a narrow near-neutral polarisation band whose width decreases with population size and increases with the strength of reciprocal/retributive tendencies. The paper presents these as novel predictions generated by the formalised RRM.

Significance. There is genuine value in this manuscript. It offers, to my knowledge, the first formal dynamical-systems treatment of propensity dynamics from RRM, extends bounded-confidence opinion dynamics with witness effects and fixed individual attractors, proves boundedness cleanly, identifies a necessary parameter region for oscillations, and is explicit about its limitations (fixed PoE, no empirical calibration, no crime rate). These are concrete strengths: the analytical results are nontrivial and the modelling framework is reusable. However, the two headline claims—persistent oscillations as the dominant behaviour and neutral-perception polarisation—rest respectively on visually assessed single simulations and on a singular threshold specification. The paper's own discussion in Section 6 concedes that the polarisation result is a direct consequence of the threshold choice. As a result, the advertised conclusions are not yet established, although the underlying framework is defensible and the issues appear fixable within the manuscript's scope.

major comments (4)
  1. [§3, Eqs. (3)/(5); §5.2; §6] The polarisation-at-neutral prediction is a singular-limit consequence of the threshold inequality. For P_i=0 the condition r_i^±|C_j-P_i|>|P_i| becomes r_i^±|C_j|>0, so every interaction with C_j≠0 triggers the extreme-pulling branch; the witness condition in (5) behaves similarly. The natural-drift branch is effectively disabled. The manuscript's Section 6 states that the result is 'a direct consequence of the threshold specification rather than an artefact of implementation,' but that is precisely the vulnerability: no criminological derivation of a zero threshold is given, and a perturbed threshold r|C_j-P_i|>c with c>0 would remove the degeneracy and may well eliminate or shrink the neutral-polarisation band. Since this band is a headline result, the paper needs either a principled justification of c=0, a robustness analysis over threshold perturbations, or a reframed claim explicit
  2. [§5.1–5.2, Figs. 2–6] The central simulation claims—that persistent oscillations are the dominant system behaviour and that the Fig. 6 grid separates consensus from polarisation—are not quantified. There is no operational definition of an oscillating trajectory as opposed to a slowly converging one, no criterion for polarisation (e.g., fraction of agents within tolerance of ±1), and no explanation of how the 'inconclusive' class in Fig. 6 is determined. Figures 2–5 show single runs without replication statistics, error bars, or convergence diagnostics, and no code or seed details are provided. Because these simulations carry the abstract's claims about dominant behaviour and phase regimes, the paper should supply precise convergence/oscillation criteria, averages over multiple realisations, and code/data availability.
  3. [§4.1, proof of Theorem 4.3] The proof after Eqs. (14)–(15) is incomplete. If j(t_m) does not converge, the statement that 'letting t→∞ ... immediately yields |C_i^∞|=1' does not follow: the update is evaluated at t_m+1, not along the subsequence t_m, and a non-convergent partner sequence can in principle produce vanishing increments if 1-|C_i| tends to zero. A complete proof should use monotonicity of C_i(t) and the δ-lower-bound on interaction products to show that infinitely many positive increments bounded below preclude an interior limit. The theorem is plausible and likely fixable, but as written this is a genuine gap in a stated sufficient condition.
  4. [§4.2] The inference from the necessary condition (19)–(20) to 'the model setup therefore enables propensity oscillations to be the dominant mode of behaviour' overstates the analytical content. Condition (20) is necessary for the shock branch to be reachable; it does not say that trajectories actually oscillate. The further claim that a truncated-normal parameter distribution yields more than half of agents capable of oscillation is an assumption about parameter priors, stated without criminological data. The dominance claim must be carried by simulations, which currently lack the quantitative support discussed above.
minor comments (5)
  1. [§5, first paragraph] The stochastic generative process is underspecified: how n(t) is sampled, whether witness assignment is with replacement, and how the 20×20 grid fixes pairings across runs would need to be stated for reproducibility.
  2. [Fig. 6 caption] The terms 'blue circle', 'red square', and 'black triangle' are not defined in the text; the classification criterion should be given explicitly, as should the choice of 10,000 steps as the convergence horizon.
  3. [§6] There are typographical slips: 'larger e_i' should be 'larger r_i^e', and 'serves as the their' should be 'serves as their'.
  4. [Eq. (20)] For r_i^±=1, the text says the range is infinite; since P_i is constrained to [-1,1], the effective range is simply [-1,1].
  5. [§6] The phrase 'direct consequence of the threshold specification rather than an artefact of implementation' is confusing. The threshold specification is a modelling choice, so this wording does not address the concern that the result may be an artefact of that choice.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the advertised polarisation and oscillation results are derived from the paper's explicit equations, with no fitted parameters and no load-bearing self-citation.

full rationale

The derivation chain is self-contained. Equations (2)--(5) define mRRM; the analytical results (Theorems 4.1, 4.3, 4.4, and the necessary condition in Section 4.2) are proved from those equations, and the oscillatory/polarisation regimes are observed in simulations of the same rules. No constant is fitted to reproduce the target phenomena; the paper explicitly states that 'the parameters are not empirically calibrated' (Section 6), so the neutral-perception polarisation is not a fitted input renamed as a prediction. The citations to Svingen (2023) supply the verbal RRM being formalised and a qualitative justification for parameter sampling, but the mathematical conclusions do not reduce to those citations. The point most likely to be mistaken for circularity--near-neutral polarisation being 'a direct consequence of the threshold specification' (Section 6)--is a transparent consequence of setting the threshold RHS to |P_i| in eqs. (3)/(5): with P_i = 0 any interaction with C_j != 0 satisfies r|C_j-P_i| > |P_i|, activating the top line. This is a modelling-assumption robustness concern (a c > 0 threshold would likely remove the effect), not a circular derivation, and the paper itself flags it as a specification consequence rather than hiding it. No enumerated circular step is therefore established.

Assumptions & free parameters 6 free parameters · 6 assumptions · 1 invented entities

The central results depend on model parameters and rule choices that are not fitted to data: reciprocity, retribution, perception-of-environment, initial propensities, and the 1/4 interaction weight. The threshold form is the most consequential ad hoc choice, because the neutral-polarisation result follows directly from setting |P_i|=0 in that threshold. The model therefore demonstrates what the assumed mechanisms can produce, not what crime dynamics are.

free parameters (6)
  • reciprocity strengths r_i^± = drawn from truncated normal distribution in [0,1]; variance unspecified; not calibrated
    Chosen by hand and sampled in simulation; no empirical calibration. The paper states parameters are 'not empirically calibrated' (§6).
  • retribution strength r_i^e = drawn from truncated normal distribution in [0,1]; variance unspecified; not calibrated
    Same as r_i^±; sampled independently in simulations.
  • perception of environment P_i = drawn from truncated normal distribution in [-1,1]; held constant
    An agent-specific attractor and a central modelling simplification; not measured empirically.
  • initial propensities C_i(0) = drawn from truncated normal distribution in [-1,1]; variance unspecified
    Initial conditions sampled independently; affects whether polarisation emerges.
  • universal r and P in §5.2 = 20×20 grid: r ∈ [0,1], P ∈ [0,0.06]
    Special-case simulation scans with r = r^e = r_i^±; used to infer the polarisation band and its scaling.
  • interaction weight coefficient 1/4 = fixed constant 1/4
    Chosen by construction so that |w| ≤ 1/2 and boundedness is easy to prove; not empirically grounded.
assumptions (6)
  • standard math Bounded monotone sequences of real numbers converge
    Used in the proofs of Theorems 4.3 and 4.4 without comment.
  • domain assumption RRM verbal theory (Svingen 2023) is a faithful qualitative account of crime causation
    The model is built to formalise RRM; if RRM's mechanisms are wrong, the model's relevance to crime is undermined.
  • domain assumption Perceptions of the environment P_i are constant over time
    Stated in §3 as the principal simplification; §7 proposes evolving P_i as future work. This is load-bearing for Theorem 4.4 and for the oscillatory balance between extreme pull and PoE return.
  • domain assumption Interaction valence is independent Bernoulli(0.5) and independent of agent propensities
    Assumed in §5; the paper notes this means mRRM models propensity rather than offending and generates no crime rate.
  • ad hoc to paper The shock threshold r|C_j−P_i| > |P_i| defines when social influence overrides natural drift
    This threshold is introduced in equations (3) and (5) without empirical or theoretical derivation; the neutral-PoE polarisation result is a direct consequence of this specification (§6).
  • domain assumption Model parameters are sampled from independent truncated normal distributions
    Stated in §5 to align with experimental results in Svingen (2023); variances and truncation are not fully specified, and no calibration is attempted.
invented entities (1)
  • Fixed perception-of-environment attractor P_i
    purpose: Provides a constant natural-drift target that competes with social influence; the competition produces oscillations and polarisation.
    P_i is a model construct adapted from RRM. The paper provides no direct empirical measurement of P_i or of its constancy; the authors acknowledge the constancy is a simplification.

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Cite this review

Pith. "Pith review of Agent-based dynamics of criminal propensity." pith.science (2026). https://pith.science/paper/LHC7UDWU

@misc{pith2026260729546,
  author       = {Pith},
  title        = {Pith review of: Agent-based dynamics of criminal propensity},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LHC7UDWU}},
  note         = {Machine review of arXiv:2607.29546}
}
read the original abstract

The Retribution and Reciprocity Model (RRM) is a novel framework presented in evolutionary criminology to understand causes of crime through the lens of cooperation. We formalise RRM as an agent-based dynamical system in which criminal propensity evolves through pairwise interactions and through observations of others' interactions. The mathematical model extends bounded confidence opinion dynamics with two novel features: an agent-specific attractor representing the perception of the environment, and the participation of third-party witnesses. We prove that individual propensities remain bounded and establish sufficient conditions for convergence to an extreme value and to the agent's perception of the environment. Simulations show that the dominant system behaviour is not convergence but persistent oscillations, a rare phenomenon in bounded confidence models arising here without the adaptive confidence bounds through which it has previously been obtained. We derive a necessary condition for oscillations, and find that a population sharing a uniform but neutral perception of the environment tends to polarise to the extremes, while a uniform non-neutral perception tends to yield consensus. The polarising regime is confined to a narrow band of near-neutral perceptions, whose width decreases with population size and increases with the strength of reciprocal and retributive tendencies.

Figures

Figures reproduced from arXiv: 2607.29546 by the authors.

Figure 1
Figure 1. The general framework of RRM, recreated from Svingen (2023). benign, some of them hostile or extreme – through the way individuals are disposed to reciprocate and to punish. The model is inherently dynamic and interactional: criminal propensity is a quantity revised through encounters with others, and revised again by those who merely witness such encounters. These features make RRM particularly suitable to a mathem… view at source ↗
Figure 2
Figure 2. Example simulation via Python of the model with 100 agents over 10,000 time steps, where only 10 agents are visualised for clarity. Parameters are fixed upon initialisation at randomised values. The common behaviour of criminal propensities oscillating around PoE values over time is illustrated, as is the rarer phenomenon of criminal propensities diverging from PoE and stabilising around extreme values (in this case… view at source ↗
Figure 3
Figure 3. Example simulations via Python of the model with 100 agents over (a) 100 time steps, and (b) 10,000 time steps. Parameters are fixed upon initialisation at randomised values, except for the universal PoE value, Pi = P for all i. 0 2000 4000 6000 8000 10000 Time step, t 1.00 0.75 0.50 0.25 0.00 0.25 0.50 0.75 1.00 Propensity, c(t) (a) r e = 0. 0 2000 4000 6000 8000 10000 Time step, t 0.75 0.50 0.25 0.00 0.25 0.50 0.7… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Example simulation via Python of the model with 100 agents over 10,000 time steps, where only 10 agents are visualised for clarity. Parameters are fixed upon initialisation at randomised values, except for either (a) the universal retribution r e i = r e , or (b) the u…
Figure 5
Figure 5. Figure 5: Example simulation via Python of the model with 100 agents over 10,000 time steps, where only 10 agents are visualised for clarity. Parameters are fixed upon initialisation at randomised values, except for either (a,b) the universal retribution r e i = r e , or (c,d) t…
Figure 6
Figure 6. Figure 6: Simulations on a 20-by-20 grid of universal r = r e = r ± and P parameter values, with either (a) 10 agents or (b) 100 agents. A blue circle denotes that the simulation outcome is consensus, a red square denotes polarisation, and a black triangle means that the outcome…

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Reference graph

Works this paper leans on

42 extracted references

  1. [1]

    APACrefauthors \ 1992

    agnew1992empirical APACrefauthors Agnew, R. APACrefauthors \ 1992 . FOUNDATION FOR A GENERAL STRAIN THEORY OF CRIME AND DELINQUENCY Foundation for a general strain theory of crime and delinquency . Criminology 30 1 47-88

    1992

  2. [2]

    APACrefauthors \ 2017

    akers1998adolescent APACrefauthors Akers, R. APACrefauthors \ 2017 . Social learning and social structure: A general theory of crime and deviance Social learning and social structure: A general theory of crime and deviance . Routledge

    2017

  3. [3]

    \ Deffuant, G

    amblard2004role APACrefauthors Amblard, F. \ Deffuant, G. APACrefauthors \ 2004 . The role of network topology on extremism propagation with the relative agreement opinion dynamics The role of network topology on extremism propagation with the relative agreement opinion dynamics . Physica A: Statistical Mechanics and its Applications 343 725--738

    2004

  4. [4]

    , Altafini, C

    bernardo2024bounded APACrefauthors Bernardo, C. , Altafini, C. , Proskurnikov, A. \ Vasca, F. APACrefauthors \ 2024 . Bounded confidence opinion dynamics: A survey Bounded confidence opinion dynamics: A survey . Automatica 159 111302

    2024

  5. [5]

    , Groff, E R

    birks2025agent APACrefauthors Birks, D. , Groff, E R. \ Malleson, N. APACrefauthors \ 2025 . Agent-based modeling in criminology Agent-based modeling in criminology . Annual Review of Criminology 8 1 75--95

    2025

  6. [6]

    , Townsley, M

    birks2012generative APACrefauthors Birks, D. , Townsley, M. \ Stewart, A. APACrefauthors \ 2012 . Generative explanations of crime: Using simulation to test criminological theory Generative explanations of crime: Using simulation to test criminological theory . Criminology 50 1 221--254

    2012

  7. [7]

    \ Felson, M

    cohen1979social APACrefauthors Cohen, L E. \ Felson, M. APACrefauthors \ 1979 . Social change and crime rate trends: A routine activity approach Social change and crime rate trends: A routine activity approach . American sociological review 588--608

    1979

  8. [8]

    , Neau, D

    deffuant2000mixing APACrefauthors Deffuant, G. , Neau, D. , Amblard, F. \ Weisbuch, G. APACrefauthors \ 2000 . Mixing beliefs among interacting agents Mixing beliefs among interacting agents . Advances in Complex Systems 3 01n04 87--98

    2000

Show all 42 references
  1. [9]

    \ Perc, M

    d2015statistical APACrefauthors D'Orsogna, M R. \ Perc, M. APACrefauthors \ 2015 . Statistical physics of crime: A review Statistical physics of crime: A review . Physics of life reviews 12 1--21

    2015

  2. [10]

    APACrefauthors \ 2006

    epstein2006generative APACrefauthors Epstein, J M. APACrefauthors \ 2006 . Generative Social Science: Studies in Agent-Based Computational Modeling Generative social science: Studies in agent-based computational modeling . Princeton University Press

    2006

  3. [11]

    \ Hirschi, T

    hirschi1990substantive APACrefauthors Gottfredson, M R. \ Hirschi, T. APACrefauthors \ 1990 . A General Theory of Crime A general theory of crime . Stanford University Press

    1990

  4. [12]

    APACrefauthors \ 2007

    groff2007simulation APACrefauthors Groff, E R. APACrefauthors \ 2007 . Simulation for theory testing and experimentation: An example using routine activity theory and street robbery Simulation for theory testing and experimentation: An example using routine activity theory and...

    2007

  5. [13]

    APACrefauthors \ 2005

    hedstrom2005dissecting APACrefauthors Hedstrom, P. APACrefauthors \ 2005 . Dissecting the social: On the principles of analytical sociology Dissecting the social: On the principles of analytical sociology . Cambridge University Press

    2005

  6. [14]

    \ Ylikoski, P

    hedstrom2010causal APACrefauthors Hedstr \"o m, P. \ Ylikoski, P. APACrefauthors \ 2010 . Causal mechanisms in the social sciences Causal mechanisms in the social sciences . Annual review of sociology 36 1 49--67

    2010

  7. [15]

    \ Krause, U

    hegselmann2002opinion APACrefauthors Hegselmann, R. \ Krause, U. APACrefauthors \ 2002 . Opinion dynamics and bounded confidence models, analysis, and simulation Opinion dynamics and bounded confidence models, analysis, and simulation . Journal of Artificial Societies and Soci...

    2002

  8. [16]

    APACrefauthors \ 2017

    hirschi1969hellfire APACrefauthors Hirschi, T. APACrefauthors \ 2017 . Causes of delinquency Causes of delinquency . Routledge

    2017

  9. [17]

    , Rocha, L E

    klymentiev2025homophily APACrefauthors Klymentiev, R. , Rocha, L E. \ Vandeviver, C. APACrefauthors \ 2025 . Homophily promotes stable connections in co-offending networks but limits information diffusion: insights from a simulation study Homophily promotes stable connections ...

    2025

  10. [18]

    \ Sampson, R J

    laub1993turning APACrefauthors Laub, J H. \ Sampson, R J. APACrefauthors \ 1993 . Turning points in the life course: Why change matters to the study of crime Turning points in the life course: Why change matters to the study of crime . Criminology 31 3 301--325

    1993

  11. [19]

    \ Dodge, K A

    lochman1998distorted APACrefauthors Lochman, J E. \ Dodge, K A. APACrefauthors \ 1998 . Distorted perceptions in dyadic interactions of aggressive and nonaggressive boys: Effectsof prior expectations, context, and boys' age Distorted perceptions in dyadic interactions of aggre...

    1998

  12. [20]

    APACrefauthors \ 2005

    lorenz2005stabilization APACrefauthors Lorenz, J. APACrefauthors \ 2005 . A stabilization theorem for dynamics of continuous opinions A stabilization theorem for dynamics of continuous opinions . Physica A: Statistical Mechanics and its Applications 355 1 217--223

    2005

  13. [21]

    , Heppenstall, A

    malleson2010crime APACrefauthors Malleson, N. , Heppenstall, A. \ See, L. APACrefauthors \ 2010 . Crime reduction through simulation: An agent-based model of burglary Crime reduction through simulation: An agent-based model of burglary . Computers, environment and urban system...

    2010

  14. [22]

    , Smith-Lovin, L

    mcpherson2001birds APACrefauthors McPherson, M. , Smith-Lovin, L. \ Cook, J M. APACrefauthors \ 2001 . Birds of a feather: Homophily in social networks Birds of a feather: Homophily in social networks . Annual review of sociology 27 1 415--444

    2001

  15. [23]

    APACrefauthors \ 1938

    merton1938anomie APACrefauthors Merton, R K. APACrefauthors \ 1938 . Anomie and social structure Anomie and social structure . American sociological review 3 5 672--682

    1938

  16. [24]

    APACrefauthors \ 1993

    moffitt1993adolescence APACrefauthors Moffitt, T E. APACrefauthors \ 1993 . Adolescence-limited and life-course-persistent antisocial behavior: a developmental taxonomy. Adolescence-limited and life-course-persistent antisocial behavior: a developmental taxonomy. Psychological...

    1993

  17. [25]

    , Guzm \'a n, E

    palma2025digital APACrefauthors Palma-Borda, J. , Guzm \'a n, E. \ Belmonte, M V. APACrefauthors \ 2025 . A digital shadow for modeling, studying and preventing urban crime A digital shadow for modeling, studying and preventing urban crime . IEEE Access

    2025

  18. [26]

    , Donnay, K

    perc2013understanding APACrefauthors Perc, M. , Donnay, K. \ Helbing, D. APACrefauthors \ 2013 . Understanding recurrent crime as system-immanent collective behavior Understanding recurrent crime as system-immanent collective behavior . PloS one 8 10 e76063

    2013

  19. [27]

    , Siev, J J

    petty2023attitude APACrefauthors Petty, R E. , Siev, J J. \ Bri \ n ol, P. APACrefauthors \ 2023 . Attitude strength: What’s new? Attitude strength: What’s new? The Spanish Journal of Psychology 26 e4

    2023

  20. [28]

    , Chaiken, S

    pomerantz1995attitude APACrefauthors Pomerantz, E M. , Chaiken, S. \ Tordesillas, R S. APACrefauthors \ 1995 . Attitude strength and resistance processes. Attitude strength and resistance processes. Journal of personality and social psychology 69 3 408

    1995

  21. [29]

    APACrefauthors \ 2013

    glenn2013antisocial APACrefauthors Raine, A. APACrefauthors \ 2013 . The Anatomy of Violence: The Biological Roots of Crime The anatomy of violence: The biological roots of crime . Pantheon

    2013

  22. [30]

    , Restrepo, J G

    sampson2025oscillatory APACrefauthors Sampson, C R. , Restrepo, J G. \ Porter, M A. APACrefauthors \ 2025 . Oscillatory and excitable dynamics in an opinion model with group opinions Oscillatory and excitable dynamics in an opinion model with group opinions . Physical Review E...

    2025

  23. [31]

    APACrefauthors \ 1971

    schelling1971dynamic APACrefauthors Schelling, T C. APACrefauthors \ 1971 . Dynamic models of segregation Dynamic models of segregation . Journal of mathematical sociology 1 2 143--186

    1971

  24. [32]

    , D'orsogna, M R

    short2008statistical APACrefauthors Short, M B. , D'orsogna, M R. , Pasour, V B. , Tita, G E. , Brantingham, P J. , Bertozzi, A L. \ Chayes, L B. APACrefauthors \ 2008 . A statistical model of criminal behavior A statistical model of criminal behavior . Mathematical Models and...

    2008

  25. [33]

    , Jackson, S E

    stokes APACrefauthors Stokes, B M. , Jackson, S E. , Garnett, P. \ Luo, G. APACrefauthors \ 2022 . Extremism, segregation and oscillatory states emerge through collective opinion dynamics in a novel agent-based model Extremism, segregation and oscillatory states emerge through...

    2022

  26. [34]

    APACrefauthors \ 1947

    sutherland1947differential APACrefauthors Sutherland, E H. APACrefauthors \ 1947 . Principles of Criminology Principles of criminology \ ( 4th \ ). Lippincott

    1947

  27. [35]

    APACrefauthors \ 2023

    svingen APACrefauthors Svingen, E. APACrefauthors \ 2023 . Evolutionary Criminology and Cooperation: Retribution, Reciprocity, and Crime Evolutionary criminology and cooperation: Retribution, reciprocity, and crime . Palgrave Macmillan

    2023

  28. [36]

    APACrefauthors \ 2025

    svingen2025 APACrefauthors Svingen, E. APACrefauthors \ 2025 . Evolutionary criminology and the future of theory Evolutionary criminology and the future of theory . Papers from the British Criminology Conference Papers from the british criminology conference \ ( 23, \ 6--21)

    2025

  29. [37]

    , Bogaerts, S

    tuente2019hostile APACrefauthors Tuente, S K. , Bogaerts, S. \ Veling, W. APACrefauthors \ 2019 . Hostile attribution bias and aggression in adults-a systematic review Hostile attribution bias and aggression in adults-a systematic review . Aggression and violent behavior 46 66--81

    2019

  30. [38]

    \ Beaver, K M

    walsh2009biosocial APACrefauthors Walsh, A. \ Beaver, K M. APACrefauthors \ 2009 . Biosocial criminology Biosocial criminology . Handbook on crime and deviance Handbook on crime and deviance \ ( \ 79--101). Springer

    2009

  31. [39]

    , Deffuant, G

    weisbuch2005persuasion APACrefauthors Weisbuch, G. , Deffuant, G. \ Amblard, F. APACrefauthors \ 2005 . Persuasion dynamics Persuasion dynamics . Physica A: Statistical Mechanics and its Applications 353 555--575

    2005

  32. [40]

    APACrefauthors \ 2006

    wikstrom2006individuals APACrefauthors Wikstr \"o m, P O H. APACrefauthors \ 2006 . Individuals, settings, and acts of crime: Situational mechanisms and the explanation of crime Individuals, settings, and acts of crime: Situational mechanisms and the explanation of crime . The...

    2006

  33. [41]

    , Oberwittler, D

    wikstrom2012breaking APACrefauthors Wikstr \"o m, P O H. , Oberwittler, D. , Treiber, K. \ Hardie, B. APACrefauthors \ 2012 . Breaking Rules: The Social and Situational Dynamics of Young People's Urban Crime. Breaking rules: The social and situational dynamics of young people'...

    2012

  34. [42]

    \ Treiber, K

    wikstrom2007role APACrefauthors Wikstr \"o m, P O H. \ Treiber, K. APACrefauthors \ 2007 . The role of self-control in crime causation: Beyond Gottfredson and Hirschi's general theory of crime The role of self-control in crime causation: Beyond gottfredson and hirschi's genera...

    2007

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