Foraging Strategies of Homo CriminalisLessons From Behavioral Ecology

Wim Bernasco — NSCR, the Netherlands, wbernasco@nscr.nl

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