Foraging Strategies of
Homo Criminalis
Lessons From Behavioral Ecology
IPAM 2007
Wim Bernasco — NSCR, the Netherlands, wbernasco@nscr.nl
Main question
Does optimal foraging theory help us
understand how offenders commit crimes?
WARNING
Psychologist talking about biology to mathematicians and criminologists
Outline
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Biological perspectives on crime and delinquency
Foraging behavior and optimal foraging theory – a
brief overview
Applications to behavioral patterns in property
crimes
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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
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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
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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
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Optimization
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decisions: what can be chosen?
currency: what is maximized?
constraints: within which limits?
Natural selection
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Optimal strategies increase fitness (survival and offspring)
Optimization in foraging theory
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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?
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Decision
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Currency
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choice = probability of eating
encountered item
maximize long-term average rate of
energy intake
Constraints
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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)
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1-0 rule
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profitability ranking
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given prey type is either always chosen or never chosen,
pr(attack) = 0 or 1
prey types are ranked by profitability (energy/time), prey
types added to diet in rank order
independence of encounter rate
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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 ?
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Hypotheses open for testing
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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?
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Decision
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Currency
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choice = how long to stay in
encountered patch
maximize long-term average rate of
energy intake
Constraints
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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)
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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
λ
(if all patches equal)
= patch encounter rate
1/λ = expected time between patches
energy
travel time
1/λ2
1/λ1
0
t1
t2
time in patch
When to leave criminal target areas?
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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
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food is carried back to a central place
example: birds feeding their young
affects (return) travel time
influences behavioral choices
Central place foraging
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foraging close to central place
distance-size relation
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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 crime
some empirical evidence
distance-gain
frequency of offences
proceeds of offences
distance decay
distance from home
distance from home
Trade-offs
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single currency (energy/time) too restrictive
‘utility’ is trade-off between
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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)
U ij    F j   W j    Pj  ...  Dij   ij
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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
Food=16
Water = 0
Predation =3
Food=3
Distance=1
Water = 3
Predation =1
Distance=4
YOUR NEST
PREDATOR
Food=9
Water = 1
FOOD
Predation =4
WATER
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
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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*
(* p  .01)
Data 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
for
Pr by factor
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
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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
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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?
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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
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Discussion
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Is optimal foraging theory different from rational
choice theory?
animals must eat (or die), humans may choose
not to offend, they have alternatives
Conclusion
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OFT is useful for generating hypotheses, but
apply with care
Thank you!
Distance decay and groep
0.50
0.45
0.40
0.35
solitary
percentage
0.30
mean distance (group)
0.25
minimum distance (group)
0.20
0.15
0.10
0.05
0.00
1
2
3
4
5
kilometres
6
7
8
Risk-sensitive foraging
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maximizing (long-term) expected energy gain
may not be optimal