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111 Long Swings in Homicide 1. 222 Outline Evidence of Long Swings in Homicide Evidence of Long Swings in Other Disciplines Long Swing Cycle Concepts:

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Presentation on theme: "111 Long Swings in Homicide 1. 222 Outline Evidence of Long Swings in Homicide Evidence of Long Swings in Other Disciplines Long Swing Cycle Concepts:"— Presentation transcript:

1 111 Long Swings in Homicide 1

2 222 Outline Evidence of Long Swings in Homicide Evidence of Long Swings in Other Disciplines Long Swing Cycle Concepts: Kondratieff Waves More about ecological cycles Models 2

3 333 Part I. Evidence of Long Swings in Homicide US Bureau of Justice Statistics Report to the Nation On Crime and Justice, second edition California Department of Justice, Homicide in California 3

4 444 Bureau of Justice Statistics, BJS “Homicide Trends in the United States, 1980-2008”, 11- 16-2011 “Homicide Trends in the United States”, 7-1-2007 4

5 555 Bureau of Justice Statistics Peak to Peak: 50 years 5

6 666 Report to the Nation ….p.15 6

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8 888

9 999 1980 9

10 10 Executions in the US 1930-2007 http://www.ojp.usdoj.gov/bjs Peak to Peak: About 65 years 10

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12 12 Part Two: Evidence of Long Swings In Other Disciplines Engineering 50 year cycles in transportation technology 50 year cycles in energy technology Economic Demography Simon Kuznets, “Long Swings in the Growth of Population and Related Economic Variables” Richard Easterlin, Population, labor Force, and Long Swings in Economic Growth Ecology Hudson Bay Company

13 13 Cesare Marchetti 13

14 14 Erie Canal

15 15 90%10% 1859 1890 1921 15

16 16 Cesare Marchetti: Energy Technology: Coal, Oil, Gas, Nuclear 52 years57 years56 years 16

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19 19 Richard Easterlin 20 year swings

20 20 Canadian Lynx and Snowshoe Hare, data from the Hudson Bay Company, nearly a century of annual data, 1845-1935 The Lotka-Volterra Model (Sarah Jenson and Stacy Randolph, Berkeley ppt., Slides 4-9) Cycles in Nature 20

21 21

22 22 What Causes These Cycles in Nature? At least two kinds of cycles Harmonics or sin and cosine waves Deterministic but chaotic cycles 22

23 23 Part Three: Thinking About Long Waves In Economics Kondratieff Wave 23

24 24 Nikolai Kondratieff (1892-1938) Brought to attention in Joseph Schumpeter’s Business Cycles (1939) 24

25 25 2008-2014: Hard Winter 25

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27 27 Cesare Marchetti “Fifty-Year Pulsation In Human Affairs” Futures 17(3):376-388 (1986) www.cesaremarchetti.org/archive/ scan/MARCHETTI-069.pdf www.cesaremarchetti.org/archive/ scan/MARCHETTI-069.pdf Example: the construction of railroad miles is logistically distributed 27

28 28 Cesare Marchetti 28

29 29 Theodore Modis Figure 4. The data points represent the percentage deviation of energy consumption in the US from the natural growth-trend indicated by a fitted S- curve. The gray band is an 8% interval around a sine wave with period 56 years. The black dots and black triangles show what happened after the graph was first put together in 1988.[7] Presently we are entering a “spring” season. WWI occurred in late “summer” whereas WWII in late “winter”. 29

30 30 Part Four: More About Ecological Cycles 30

31 31 Well Documented Cycles 31

32 32 Similar Data from North Canada 32

33 33 Weather: “The Butterfly Effect”

34 34 The Predator-Prey Relationship Predator-prey relationships have always occupied a special place in ecology Ideal topic for systems dynamics Examine interaction between deer and predators on Kaibab Plateau Learn about possible behavior of predator and prey populations if predators had not been removed in the early 1900s

35 35 NetLogo Predator-Prey Model

36 36 36 Crime Generation Crime Control Offense Rate Per Capita Expected Cost of Punishment Schematic of the Criminal Justice System: Simultaneity Causes ? (detention, Deterrence, Rehabilitation, And revenge) Expenditures Weak Link OF = f(CR, SV, CY, SE, MC) CR = g(OF, L)

37 37 Source: Report to the Nation on Crime and Justice Expect Get 37

38 38 Questions? How to Model?

39 39 Part Five: The Lotka-Volterra Model Built on economic concepts Exponential population growth Exponential decay Adds in the interaction effect We can estimate the model parameters using regression We can use simulation to study cyclical behavior

40 40 Lotka-Volterra Model Vito Volterra (1860-1940) famous Italian mathematician Retired from pure mathematics in 1920 Son-in-law: D’Ancona Alfred J. Lotka (1880-1949) American mathematical biologist primary example: plant population/herbivorous animal dependent on that plant for food

41 41 Predator-Prey 1926: Vito Volterra, model of prey fish and predator fish in the Adriatic during WWI 1925: Alfred Lotka, model of chemical Rx. Where chemical concentrations oscillate 41

42 42 Applications of Predator-Prey Resource-consumer Plant-herbivore Parasite-host Tumor cells or virus-immune system Susceptible-infectious interactions 42

43 43 Non-Linear Differential Equations dx/dt = x(α – βy), where x is the # of some prey (Hare) dy/dt = -y(γ – δx), where y is the # of some predator (Lynx) α, β, γ, and δ are parameters describing the interaction of the two species d/dt ln x = (dx/dt)/x =(α – βy), without predator, y, exponential growth at rate α d/dt ln y = (dy/dt)/y = - (γ – δx), without prey, x, exponential decay like an isotope at rate  43

44 44 Population Growth: P(t) = P(0)e at

45 45 lnP(t) = lnP(1960) + at

46 46 CA Population: exponential rate of growth, 1995-2007 is 1.4%

47 47 Prey (Hare Equation) Hare(t) = Hare(t=0) e a*t, where a is the exponential growth rate Ln Hare(t) = ln Hare(t=0) + a*t, where a is slope of ln Hare(t) vs. t ∆ ln hare(t) = a, where a is the fractional rate of growth of hares So ∆ ln hare(t) = ∆ hare(t)/hare(t-1)=[hare(t) – hare(t-1)]/hare(t-1) Add in interaction effect of predators; ∆ ln Hare(t) = a – b*Lynx So the lynx eating the hares keep the hares from growing so fast To estimate parameters a and b, regress ∆ hare(t)/hare(t-1) against Lynx

48 48 Hudson Bay Co. Data: Snowshoe Hare & Canadian Lynx, 1845-1935

49 49 [Hare(1865)-Hare(1863)]/Hare(1864) Vs. Lynx (1864) etc. 1863-1934 ∆ hare(t)/hare(t-1) = 0.77 – 0.025 Lynx a = 0.77, b = 0.025 (a = 0.63, b = 0.022)

50 50 [Lynx(1847)-Lynx(1845)]/Hare(1846) Vs. Lynx (1846) etc. 1846-1906 ∆ Lynx(t)/Lynx(t-1) = -0.24 + 0.005 Hare c = 0.24, d= 0.005 ( c = 0.27,d = 0.006)

51 51 Simulations: 1845-1935 Mathematica http://mathworld.wolfram.com/Lotka- VolterraEquations.htmlhttp://mathworld.wolfram.com/Lotka- VolterraEquations.html Predator-prey equations Predator-prey model

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54 54 Simulating the Model: 1900-1920 Mathematica a = 0.5, b = 0.02 c = 0.03, d= 0.9

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57 57 Part Six: A Lotka-Volterra Model For Homicide? Do other violent crimes move with homicide?

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59 59 Distribution of Ratio of Rape to Homicide; Median = 4.2

60 60 Ratio of Rapes to Homicides

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62 62 Part Six: A Lotka-Volterra Model For Homicide? Do other violent crimes move with homicide? We have a measure of the rabbits: homicides. How about a measure for the foxes (coyotes)?

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66 66 Fractional Change in California Prisoners 1860-2009 Trough to trough 16 years, a half a cycle

67 67 Fractional Change in California Prisoners 1930-2009 Trough to trough 18 years, a half a cycle

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