Read the research study by Thibodeau and Boroditsky published in PLoS 1 and the associated newspaper article Metaphors can change our opinions in ways we don’t realize by S. Rathje. While reading these articles, keep in mind our own studies this semester, and see if you can find a way to apply it to these readings.

Quiz - the impact of metaphors for crime

The results of Thibodeau and Boroditsky and other researchers suggest that metaphors are powerful communications tools, providing a compact way of communicating a significantly more complex phenomena. However, the specific meanings of metaphors can be elusive. We all have some visceral feel of what it means for a predator to be lurking in the dark waiting to pounce on us, and how it feels during flu season, but it takes a fair amount of work to unpack those feelings and translate them into a specific and tangible way. Let \(N\) be the count regular city residents, \(V\) be the count of crime victims.

**Predator-crime model**

For the predator metaphor, we let \(P\) be the count of criminals who prey upon the city.

\[\begin{align} \dot{N} &= -c P N, \\ \dot{V} &= c P N, \\ \dot{P} &= \eta - \gamma P. \end{align}\]

The effective interventions will reduce opportunities for crime (reduce \(c\)), reduce the number of predators by slowing immigration (reduce \(\eta\)), or accelerating incarceration (increase \(\gamma\)).

**Disease-crime model**

For the disease metaphor, let \(I\) be the city residents infected with a need to commit crimes.

\[\begin{align} \dot{N} &= -\beta I N - a N + r I, \\ \dot{V} &= \beta I N, \\ \dot{I} &= a N - r I . \end{align}\]

The effective interventions will reduce opportunities for crime (reduce \(\beta\)), reduce the number of criminals by improving well-being and reducing the motivations for criminal activity (reduce \(a\), increasing \(r\)) .

These models do capture some of the differences implicit in the predator/disease metaphors used to describe disease

Which metaphor is correct? We all have different personal experiences that give us reasons to see the world through one or the other of these metaphors. And each metaphor leads to different ideas and conclusions. But crime is a real phenomena. We have to talk about it and confront it.

The simple data we have on the rates of crimes does *not* help us distinguish between these two models. Both can be made to fit the crime rates equally well.

How do we decide which frame is the right one for analyzing the problem? When we only can talk in terms of these metaphors, this is a hard question. But once we have our models of these metaphors, we actually now also have a more powerful language for discussing crime. We do *not* have to pick one or the other metaphor. We can build a single model that incorporates the important aspects of both metaphors.

\[\begin{align} \dot{N} &= -\beta I N - c P N - a N + r I, \\ \dot{I} &= a N - r I - y I,\\ \dot{P} &= \eta - \gamma P, \\ \dot{V} &= \beta I N + c P N. \end{align}\]

Within a single model, we can now drill down and seek out data that will quantify the rates and impacts of different components. We can go a step further and try to model the costs and effectivenesses of different interventions, as well.

Not only that. The model gives us a new frame in which we can imagine the impacts of ideas that we hadn’t considered before like the potential benefits of social safety nets, the secondary effects of incarceration, and the potential for recidivism.

Can you think of any media or experiences that you have liked or disliked that pose implicit metaphors for thinking about the world?

- NCAAMB/NBA tournament
- the world is ordered, and there is a BEST team. The tournament bracket frames the entire process – there will always be one undefeated team at the end. By DEFINITION, there is a unique best.
- Rock-paper-scissors is an alternative metaphor. In rock-paper-scissors, there is no perfect strategy – every strategy loses against one of the other strategies. It could be that there are 3 teams the play different styles, and that which team ends up winning the tournament depends stronly on the initial seating.

- computer games (how to build a civilization)
- at the end of the game, do you understand the rise and fall of civilizations?
- These games DO have some realistic components, like having to interact directly only with your neighboring civilizations

- stock market as metaphor for US happiness (market is up/down)
- television (
*Survivor*– no cooperation) - movies (lone hero/heroine metaphor)

How do you discuss something without a metaphor for it?

Semi-mathematical model of World view:

Social mobility models:

- Milton Bradley’s 1960’s game of life board game
- an “invisible script” about how your life should go?

The sims

- american dream - hard work should lead to satisfaction (free will, all individuals independently, game-of-life boardgame)
- Meritocracy: we all pull yourselves up by our own boot straps
- Meritocracy: we all pull yourselves up by our own boot straps

pioneering/first-mover with preferential attachment

[Show code]

```
#!/usr/bin/python
from matplotlib.pyplot import *
from numpy import array
from random import sample
import time
"""
Incomplete
"""
m = 4
Tmax = 400
def main():
# creation
hist = array([0]*Tmax)
pool = array([0]*Tmax)
counts = [ ]
# initialization
for i in range(m):
counts.extend( [i]*(m-1) )
hist[i] = (m-1)
pool[m-1] = m
x = array(list(range(1,Tmax+1)))
fig = figure(figsize=(18,6))
subplot(1,3,1)
width = .7
xw = x-width*.5
bar_ob = bar(xw, hist, width, color='r')
ylim(0,Tmax/4)
xlim(0,Tmax)
title('Num friends per actor')
subplot(1,3,2)
bar_ob2 = bar(xw, pool, width, color='g')
ylim(0,Tmax/4)
xlim(0,Tmax/3)
title('Num actors of degree')
subplot(1,3,3)
loglog(xw,1e2/xw**3,'k-')
loglog(xw,1e3/xw**3,'k-')
loglog(xw,1e4/xw**3,'k-')
ll = loglog(xw,pool,'o')
title('loglog degree plot')
xlim(1,1e3)
ylim(1,1e2)
show(block=False)
for t in range(m,Tmax):
friends = []
for i in range(m):
newfriend = sample(counts,1)[0]
while newfriend in friends:
newfriend = sample(counts,1)[0]
friends.append(newfriend)
counts.extend(friends)
for i in friends:
hist[i] += 1
pool[hist[i]] += 1
pool[hist[i]-1] -= 1
counts.extend([t]*m)
hist[t] = m
pool[m] += 1
for rect, h in zip(bar_ob, hist):
rect.set_height(h)
for rect, h in zip(bar_ob2, pool):
rect.set_height(h)
ll[0].set_data(xw,pool)
fig.canvas.draw()
time.sleep(0.1)
main()
```

- yard-sale model: random world, pure luck

[Show code]

```
#import matplotlib
#matplotlib.use('gtkagg')
#matplotlib.use('gtk')
#matplotlib.use('wx')
from numpy import *
from numpy.random import rand, randint
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import time
plot = plt.semilogy
plot = plt.plot
beta = 0.08
n = 40
x = array(list(range(n)))+1
y = ones(n)
y = y + 1e-7*rand(n) # avoids warning about collapsed y limits # 2018-04
def update():
"""
Randomly pick two players. These
players randomly exchange $v dollars
"""
i, j = randint(0,n), randint(0,n)
if i == j:
return
#v = beta*min(y[i],y[j])
v = min(y[i],y[j],beta)
i, j = (i,j) if rand() < 0.5 else (j,i)
y[i], y[j] = y[i] + v, y[j] - v
def genData(w=0):
while w < 2000:
w+=1
for t in range(n):
update()
z = y.copy()
z.sort()
z = flipud(z)
yield w, y, z
def updater(data):
t, y, z = data
g1.set_data(x,y)
g2.set_data(x,z)
g3.set_data(x,z)
#plt.draw()
#plt.savefig('v2frame%03d.png'%w)
#time.sleep(.01)
fig = plt.figure(1, figsize=(12,6))
plt.subplot(1,3,1)
g1 = plot(x,y,'go').pop()
plt.ylim(1e-5,20)
plt.xlim(0,n)
plt.subplot(1,3,2)
g2 = plot(x,y,'ro-').pop()
plt.xlim(0,n)
plt.ylim(1e-5,20)
plt.subplot(1,3,3)
g3 = plt.loglog(x,y,'ro-').pop()
plt.xlim(1,n)
plt.ylim(1e-5,20)
ani = animation.FuncAnimation(fig, updater, genData, interval=10)
plt.show()
```

- Poverty traps
- Isolated
- Community driven

Newtonian mechanics – the world is deterministic, predictable, driven by cause and effect (catenary), explained by a universal theory

Statian – the world is random and only knowable in an average sense (meteorites)

Nonlinear dynamics – steady states and bifurcations

Chaos, game of life, … complexity sometimes emerges from simple rules

Game theorist – competition where payoffs are maximized by anticipating the actions of others