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  • 1. Ballester, Coralio
    et al.
    Zenou, Yves
    Stockholm University, Faculty of Social Sciences, Department of Economics. Research Institute of Industrial Economics, Sweden.
    Key Player Policies When Contextual Effects Matter2014In: The Journal of mathematical sociology, ISSN 0022-250X, E-ISSN 1545-5874, Vol. 38, no 4, p. 233-248Article in journal (Refereed)
    Abstract [en]

    We consider a model where the criminal decision of each individual is affected by not only her own characteristics, but also by the characteristics of her friends (contextual effects). We determine who the key player is, i.e., the criminal who once removed generates the highest reduction in total crime in the network. We propose a new measure, the contextual intercentrality measure, that generalizes the one proposed by Ballester, Calvo-Armengol, and Zenou (2006) by taking into account the change in contextual effects following the removal of the key player. We also provide an example showing that the key player can be different whether contextual effects are taken into account or not. This means that the planner may target the wrong person if it ignores the effect of the context when removing a criminal from a network.

  • 2.
    Ehn, Micael
    Stockholm University, Faculty of Humanities, Centre for the Study of Cultural Evolution.
    Temporal discounting leads to social stratification2012In: The Journal of mathematical sociology, ISSN 0022-250X, E-ISSN 1545-5874, Vol. 36, no 4, p. 245-255Article in journal (Refereed)
    Abstract [en]

    Social stratification is present in all modern societies. Do income differences simply reflect inherited differences in individual abilities and resources? If not, why does not everyone choose strategies that lead to high income? This article shows that the psychological phenomenon known as temporal discounting will lead to differences in educational attainment and social stratification in any society where the demand for workers with a higher level of education is higher than for those with a lower level. The model is used to predict income differences between people with and without college education in seven developed countries, based only on official statistics of the cost and length of college education. The model explains 93% of the variance, strongly suggesting that temporal discounting is a major factor behind income differences.

  • 3.
    Eriksson, Kimmo
    Stockholm University, Faculty of Humanities, Centre for the Study of Cultural Evolution. Mälardalen University, Sweden.
    The Accuracy of Mathematical Models of Justice Evaluations2012In: The Journal of mathematical sociology, ISSN 0022-250X, E-ISSN 1545-5874, Vol. 36, no 2, p. 125-135Article in journal (Refereed)
    Abstract [en]

    Jasso (1978) proposed a universal law of justice evaluations describing a logarithmic relationship between the perceived injustice of a reward and the ratio between this reward and the just reward. In applications this model is treated as if it were exact, whereas analogous models in psychophysics have empirically established degrees of uncertainty. In this article I make the first assessment of the magnitude of error in the logarithmic model of justice evaluations, using published data and a novel experiment. For the standard application of the model, where just rewards are inferred from justice evaluations, I find that the inherent inaccuracy leads to errors of about 15% on average. I also compared the logarithmic model to 2 nonlogarithmic models. Almost 20% of my respondents made justice evaluations that were more consistent with one of the latter models, suggesting that no single model is really universal.

  • 4.
    Frank, Ove
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Carrington, Peter J.
    Estimation of offending and co-offending using available data with model support2007In: The Journal of mathematical sociology, ISSN 0022-250X, E-ISSN 1545-5874, Vol. 31, no 1, p. 1-46Article in journal (Refereed)
    Abstract [en]

    Police data under-report the numbers of crimes and of offenders, the numbers of offenders participating in individual criminal incidents (incident sizes) and the numbers of incidents in which individual offenders participate (offender activity). Criminal participation in incidents is a concept that underlies and unifies all of these phenomena, so that the numbers of incidents and of offenders, and incident size distributions and offender activity distributions, can all be derived from the criminal participation matrix. Two related probability models are presented that permit the estimation of numbers of incidents and offenders, incident size distributions, offender activity distributions, and co-offending distributions, from police-reported crime data, and data on the reporting of crime to police. The models are estimated, using data from the Canadian Uniform Crime Reporting Survey and national victimization surveys for the period 1995 - 2001.

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