Michael Makowsky (2006)
An Agent-Based Model of Mortality Shocks, Intergenerational Effects, and Urban Crime
Journal of Artificial Societies and Social Simulation
vol. 9, no. 2
<https://www.jasss.org/9/2/7.html>
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Received: 05-Oct-2005 Accepted: 03-Feb-2006 Published: 31-Mar-2006
Quaint old town of toil and traffic, quaint old town of art and song, Memories haunt thy pointed gables, like the rooks that round them Throng
from "Nuremburg," by Henry Wadsworth Longfellow
If I knew I was going to live this long, I'd have taken better care of myself.
Mickey Mantle
Major consistent differences in subjective life expectancy…were found between successful and unsuccessful employees…Employees who failed to stay on the job were found to have shorter lifespan predictions.In short, those with longer SLE's were more interested in building human capital in the form of occupational experience and tenure. The notion of an agent's time horizon as related to his propensity, or potential, to commit crime was explored by Banfield (1970), casting the criminal as being more "present oriented" - essentially agents with higher discount rates of time are more likely to weigh the benefits of crime higher than the costs. Discounting and temporal factors are explored in much greater detail by Wilson and Herrnstein (1985), noting that crime, versus non-crime, is differentiated by benefits preceding costs. Wilson presents this differentiation as a set of benefit and cost curves based on the laboratory studies of Farrington and Knight (1979). It is with these results in mind that the model is initially parameterized.
(1) |
where a is the lump sum payment received for choosing the occupation, b is the income received each turn in the simulation, and c is the turns spent pre-employment.[9] Agents discount future utility at a uniform interest rate, r, of 3%. The heuristic for SLE, Expected Lifespan, E(Li) [Formula 2] is a function of the average lifespan,, of each patch, j, in the agents neighborhood of ni total patches.
(2) |
The lump sum a is positive for the criminal (pecuniary and non-pecuniary benefits inclusive), zero for the laborer, and negative for the professional (education costs). These parameters are in alignment with the experimental findings of Farrington and subsequent analysis of Wilson. Income is an adjustable parameter for each occupation, but for simulations pursued here it is assumed that laborers earn a yearly income greater than criminals, and professionals in turn earn an income greater than laborers. In such a model what matters is not absolute lifespan, but rather lifespan relative to the lump sum benefits of crime, the costs of education, and the lifetime earnings potential of skilled and unskilled occupations. In such a world crime can persist in the face of a growing economy, so long as the a's keep pace with the b's.
Figure 1. Runs #1, 2, 3, and 4 of 100. Each compares all four regions. Region #1 experience a morbidity shock in from time 40 until time 49. Regions 2 - 4 were not shocked, but 2% of the adult population moves each turn |
Figure 2a. The mean percentage of each region with a criminal career, across 100 runs, each consisting of 200 turns, with a 10 turn mortality shock in region 1 at turn 40 |
Figure 2b. Control Experiment; The mean percentage of each region with a criminal career, across 100 runs, each consisting of 200 turns, without the mortality shock. Note that the peak (~ 28%) is considerably lower than in 2a, as is the final rate (~ 14%) The rate of crime rises early while information is limited, but drops naturally as the history develops and agent information sets come to reflect the underlying probabilities governing the model |
Figure 3. The variance of the percentage of each region with a criminal career, across 100 runs, each consisting of 200 turns, with a 10 turn mortality strike at turn 40 |
Figure 4. from Lafree (1999), Murder and Robbery Rates, 1946 -1997, data supplied by the U.S. Federal Bureau of Investigation, "Crime in the United States," Uniform Crime Reports annual, 1946 to 1997 (Washington D.C., Government Printing Office) |
Table 1: Rationality Control Experiment | |||||
Turns Completed | 50 | 100 | 150 | 200 | 250 |
Mean Differential (SLE - Mean Lifespan ) | -1.73 | 3.61 | 3.83 | 3.08 | 2.59 |
Percent Differential (Differential / Mean Lifespan) | -3.64% | 7.24% | 7.52% | 5.95% | 4.96% |
2 CAMSIM is designed within the NetLogo application (Wilensky 1999)
3 Simon refers to this as "'intendedly' rational"
4 In making an explicit effort to bound the rationality of agents there remains the valid critique that CAMSIM may be leaving behind agents of perfect omniscience only to employ agents of staggering myopia. This paper contends that 1) The other end of the spectrum must be visited before the truth can be found in the middle, 2) A rational agent of relative myopia bears a closer resemblance to reality than homo economicus and 3) The limitations placed upon agents in forming subjective determinations of life expectancy have been validated by empirical work to be discussed later in the paper.
5 Heterogeneity of agent discount rates could result in "genetic" drift leading directly to regional differences in crime rate, as well as various evolutionary selection possibilities. Uniform discount rates control for this.
6 This is sometimes synonymously referred to as "verification," but programmers will also use verification to refer to confirming that the program code is executing as intended.
7 Succinctly put, calibration requires the correct objective/utility function, whereas validation only requires that the form of the function is appropriate and relevant.
8 If an agent reaches age 89 he will automatically perish upon the subsequent turn.
9 This will be familiar to many as the formula for the present value of an annuity. Utility is calculated subtracting years already lived, sixteen, from the expected total. An additional 2 years are subtracted from laborers to account for finishing secondary school and, and an additional 6 years are subtracted from professionals to account for finishing high school and a college degree.
10 Less shocking, but no less important is the assumption that agent choices regarding career are irreversible. The model becomes unwieldy if agents are allowed to re-optimize later in their life.
11 One row and one column of patches were used as regional "border" patches
12 This is a generic shock; it can be interpreted as representative of a number of potential "shocks" to an urban area, such as drug-related violence, war and accompanying conscription, or an environmental health crisis.
13 Observing representative runs in conjunction with variance and spread can be useful. The real world is always "one run" as alternate realities are not typically readily available for analysis. The data must be confirmed as non-anomalous in general structure, but means and medians can "smooth away" useful information regarding how the artificial history plays out.
14 This is unlikely in this case, as the shocked region has considerably reduced population density.
15 It has always been very difficult for proponents of genetic and aptitude theories of criminal propensity to explain significant booms or busts as gene pools are not so easily shifted.
|
(3) |
Here the weighted utility, Uweighted, is a function of Utility and the reference group weighting. When an agent chooses a career (professional, laborer, or criminal) she weights the expected utility of each career: α if the none of the agents in her neighborhood have that career, 1 if at least one neighborhood agent has it, and λ if it is the most popular career in the neighborhood. When the experiments from this paper were duplicated using the reference group weightings, with α = 0.98 and λ = 1.02, the results were similar in character to the previous results, though the magnitudes of all of the criminal percentages were reduced proportionately.
Figure A1 and A2. The mean (A1) and variance (A2) of the criminal percentage of the population over 200 turns, with a shock at turn 50, using the reference group weightings, α = 0.98 and λ = 1.02 |
(1) Main loop While time < 201 For each patch [p] p.calculateAverageLifespan Random (0.02 * total population) agents move to a new location For each agent [a] { If 16 < age < 45 and (random number from 1 to 1000) < birthrate) [add new agent ] Agents-grow-up } Stats-collector If 39 < time < 50 [random 15 of region-one agents die] End-while (2) Agents-grow-up procedure For each agent[a] If a = child [ Age++ If (random number from 1 to 1000) < childDeathRate [agent dies] If age = 16 [ ExpectedLife = mean [AverageLifespan from patches in Moore Neighborhood] Calculate-Expected-Utilities choose career with maximum Expected Utility if local patch open [move to local patch] else [if random patch open [move to random patch]] ] If a = adult [ Age++ If age < 50 [If (random number from 1 to 1000) < a.career.adultDeathRate [a.dies]] If 50 <= age < 90 [If (random number from 1 to 1000) < a.career.adultDeathRate + 20 [a.dies] If age >=90 [a.dies] ] (3) Calculate-Expected-Utilities [note: the jail function has been turned off in CAMSIM for this experiment, so the expected jail time is zero) a.ExpUtilityProfessional = ((-4 * EducationCost) + ((ProIncome * (1 - exp(- rate * (ExpectedLife - 22 )))) / (exp(rate) - 1))) a.ExpUtilityLabor = ((LaborIncome * (1 - exp(- rate * (ExpectedLife - 18 )))) / (exp(rate) - 1)) a.ExpUtilityCriminal = (CrimeLumpSum + ((CrimeIncome * (1 - exp(- rate * (ExpectedLife - 16 - ExpectedJail )))) / (exp(rate) - 1)))
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