foraging strategies of homo criminalis lessons from behavioral ecology wim bernasco — nscr, the...
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Foraging Strategies of Homo CriminalisLessons From Behavioral Ecology
Wim Bernasco — NSCR, the Netherlands, [email protected]
IPAM 2007
Main question
Does optimal foraging theory help us understand how offenders commit crimes?
WARNING
Psychologist talking about biology to mathematicians and criminologists
Outline
Biological perspectives on crime and delinquency Foraging behavior and optimal foraging theory – a
brief overview Applications to behavioral patterns in property
crimes where to search what to choose how long to stay vigilance and the trade-off with safety social foraging
Biological perspectives on crime and delinquency physiological factors involved in delinquency plant ecology (Chicago School) as a model of
human populations evolutionary psychology and human
behavioral ecology Marcus Felson’s (2006)
Optimal foraging theory questions How do animals search
for food? What do animals eat? Where do animals eat? How long do animals
stay in a patch?
What affects feeding behavior?
Components of optimal foraging theory
Optimization decisions: what can be chosen? currency: what is maximized? constraints: within which limits?
Natural selection Optimal strategies increase fitness (survival and offspring)
Optimization in foraging theory
Decisions what to eat? how long to stay in a patch? where to search?
Currency long term expected energy gain per time unit while foraging extensions : survival, defense, mating …
Constraints not eat and search at the same time searching and handling of prey takes time and energy
Search or eat? Decision
choice = probability of eating encountered item
Currency maximize long-term average rate of
energy intake Constraints
cannot search and eat at same time encounter is a Poisson process energy, handling and encounter
exogenous encounter without attack is free ‘complete information’, perfect prey
recognition
Search or eat (model implications) 1-0 rule
given prey type is either always chosen or never chosen, pr(attack) = 0 or 1
profitability ranking prey types are ranked by profitability (energy/time), prey
types added to diet in rank order independence of encounter rate
inclusion of prey depends on its own profitability and on profitability of higher ranked prey types, but not on encounter rate
What to steal? Specialize or not ? Hypotheses open for testing
Target choice can be broad or selective, but it is consistent (items are always or never taken)
Items are ranked in terms of profitability (i.e on basis of CRAVED)
High availability of low ranked items does not create demand
When opportunities for stealing highly ranked items decrease, offenders become more versatile (less selective)
How long to stay? Decision
choice = how long to stay in encountered patch
Currency maximize long-term average rate of
energy intake Constraints
cannot search and eat at same time encounter is a Poisson process encounter rates with patches are
exogenous negatively accelerated ‘gain function’ ‘complete information’, perfect patch
type recognition
How long to stay (model implications) marginal value theorem: leave patch when
marginal rate (energy/time) drops to average habitat rate
marginal rate at patch exit same for all patches visited
longer in patches when travel times increase
Marginal value theorem(if all patches equal)
time in patch
energy
travel time 1/λ1t11/λ2
t2
λ = patch encounter rate
1/λ = expected time between patches
0
When to leave criminal target areas? Stay longer in places that are more profitable Stay longer if travel times between targets or
target places are larger Stay longer if the access time and costs are
higher
Central place foraging
food is carried back to a central place example: birds feeding their young affects (return) travel time influences behavioral choices
Central place foraging
foraging close to central place distance-size relation
short distance: light prey items long distance: heavier prey items
stay longer in distant patches more selective diet in distance
patches
Central place foraging in crimesome empirical evidencedistance decay
distance from home distance from home
proc
eeds
of
offe
nces
freq
uenc
y of
off
ence
s
distance-gain
Trade-offs
single currency (energy/time) too restrictive ‘utility’ is trade-off between
nutrition value travel time hydration (water) risk of predation other important things (mating, child care)
optimal decisions apply multiple criteria from a priori to a posteriori currencies
Where would you forage?
YOUR NEST
PREDATOR
FOOD
WATER
Discrete Choice Framework (RUM)
ijijjjjij DPWFU ...
choose 1 out of J alternatives actor i chooses j yielding max Uij (‘utility’) Uij function of Food, Water, Predation risk, distance,
random error Food, Water, Predation risk and Distance decision
criteria , , , and indicate direction and weight in decision
(estimated a posteriori)
Discrete spatial choice model
YOUR NEST
PREDATOR
FOOD
WATER
Food=16
Water = 0
Predation =3
Distance=1
Food=3
Water = 3
Predation =1
Distance=4
Food=9
Water = 1
Predation =4
Distance=3
Residential Burglary Target Area Choice
Attractiveness of target areas Affluence
mean value properties % home-ownership
Lack of social control residential mobility ethnic heterogeneity
Proximity and familiarity proximity to home address proximity to city center
Opportunities Number of residential units
Attributes of offenders
Ethnic origin non-native versus native
Age minor versus adult
Results of Basic Model (All Burglars)Increase of
In attribute
Changes probability by factor
1000 Residential units 1.35*
10% Residential mobility 0.98
10% Ethnic heterogeneity 1.16*
€100,000 Real estate value 1.29
10% Home ownership 1.01
1 km Proximity 1.68*
1 km Proximity to CBD 0.88*
)01.(* pData note: police records, 269 burglars, 548 solitary burglaries in 89 neighborhoods in The Hague, The Netherlands
Importance criteria by offender types Increase of
In attribute
Changes Pr by factor
for
10% Ethnic heterogeneity 1.11 natives
10% Ethnic heterogeneity 1.21* non-natives
1 km Proximity 1.62* adults
1 km Proximity 1.96* minors
Social foraging
Robinson Crusoe models animals often forage in groups game theory (optimization equilibrium) what can we learn about crimes, offender groups,
collaboration, proceeds, distances and optimal group size?
Unfortunately, not much
cooperative foraging is very rare in animals (lions, bees, ants …)
foraging apart together is the rule animals compete over food, and even if they
cooperate, they ‘cheat’ and ‘steal’ from each other
Why does this not fit (property) crime? property offending is not competitive (not
zero-sum game amongst offenders) competition only plays a role in illegal
markets (drugs-dealing, prostitution) interesting, but not useful in explaining
causes and effects of cooperation in property offending
Discussion and conclusion
Discussion Is optimal foraging theory different from rational
choice theory? animals must eat (or die), humans may choose
not to offend, they have alternatives Conclusion
OFT is useful for generating hypotheses, but apply with care
Thank you!
Distance decay and groep
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
1 2 3 4 5 6 7 8
kilometres
percentage
solitary
mean distance (group)
minimum distance (group)
Risk-sensitive foraging
maximizing (long-term) expected energy gain may not be optimal