Dynamic Foraging Model
for Human Behavior on
the internet (working title)
Bjarne Berg
Agenda
Introduction and Background
Optimal Foraging Theory (OFT)
Foraging Models
Economic perspective (EMH and REH)
Human Factors
Proposed Framework
Question and Answers
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Background
Despite extensive research in the human evolution since 1854 when
Charles Darwin published his thesis on the Origin of the Species, the
motivation and the behavior associated with foraging and thereby the
natural selection through the survival of the best foragers, was not well
researched for over one hundred years.
This changed in 1966 when researchers such as Emlem published his
work on the foraging behavior of birds and by the publication the same
year of MacArthur & Pianka’s work on the individual’s selection process
of foraging areas. Over the next thirty years this research gave rise to a
large field known as Optimal Foraging Theory (OFT) that has been the
foundation in a variety of biological and zoology studies.
Only in the last ten years OFT has been extended into the field of
information technology and search algorithms (Sugawara and
Watanabe, 2002; Pui and Huosheng, 2002).
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Background
At the same time, there has been an increased interest in the last
decade of extending OFT into a better understanding of human
behavior on the internet through intelligent foraging agents (Jiming
et. al., 2004) and through extensions to social behaviors of foraging
agents (Andrews, 2007).
Most of this research has focused on optimizing the algorithms of
robots or intelligent agents that can, on behalf of the human, scan
vast amounts of information to find specific items.
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Agenda
Introduction and Background
Optimal Foraging Theory (OFT)
Foraging Models
Economic perspective (EMH and REH)
Human Factors
Proposed Framework
Question and Answers
4
The Optimal Foraging Theory (OFT)
In 1966 the field known as Optimal Foraging Theory (OFT) was
established through the publication of Emlen’s article on foraging
behavior of birds and by MacArthur & Pianka’s work on optimization
models the same year.
In general, the models established over the next ten years focused on
four core areas that became known as elements of a micro-ecological
theory. These areas include
1) What to eat (optimal diet).
2) Where to eat (optimal patch choice).
3) Optimal allocation to each patch (time).
4) Optimal patterns and speed of movements.
Combined as a whole, the micro-ecological theory forms the platform for
macro-ecological theory which has far reaching implications
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The Optimal Foraging Theory (OFT)
Natural Selection – The optimal should already be here
Cost- Benefit and minimal benefit requirements
Optimal Patch Choice (OPC)– scholastic models (bird’s patch selection)
Committed exclusions and logical progressions
Sub-optimal foraging – social and cultural constraints
Compression Hypothesis - as the number of competing species
increase, a reduction in the patches used occur & the range of items
consumed remains constant or only slightly increase .
Specialization – Koala Bears (increased food abundance leads to greater
food preference)
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Agenda
Introduction and Background
Optimal Foraging Theory (OFT)
Foraging Models
Economic perspective (EMH and REH)
Human Factors
Proposed Framework
Question and Answers
7
Foraging Models
Optimal Diet Theory (ODT) - also includes advanced mechanisms for
gradual shifts in item acquisitions when preferable items (high net benefit)
exists or becomes more abundant.
Optimal Time Allocation (OTA) and Marginal Value Theorem (Chernov, 1976),
and Surrender Time
Evolutionary foraging algorithms
The simplest approach to account for the dynamically changing
environment has been to introduce uncertainty/variability into the
approach and rebuild the new optimal search patterns and speed of
movements each time a foraging event on a patch, or set of patches, are
completed (Yang & Yao, 2005). This was a focus area in the OFT research
field in the late 1990s and 00s. The number of recent models proposed
using this approach are numerous (Branke, 2002; Jin & Branke, 2005;
Tin´os & Yang, 2005).
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Evolutionary Foraging Algorithms (EA)
1.
Bacterial foraging algorithms (BFA) and Dynamic BFA
(DBFA)
2.
Group foraging theory and diversity in Evolutionary
algorithms
3.
4.
Dynamic and Memory enhanced foraging algorithms (E. Coli)
Thermodynamical Genetic Algorithm (TDGA)
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Agenda
Introduction and Background
Optimal Foraging Theory (OFT)
Foraging Models
Economic perspective (EMH and REH)
Human Factors
Proposed Framework
Question and Answers
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EMH and REH
1.
Efficient Market Hypothesis
2.
Rational Expectance Hypothesis
3.
Price Dispersion and 2-step models
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Agenda
Introduction and Background
Optimal Foraging Theory (OFT)
Foraging Models
Economic perspective (EMH and REH)
Human Factors
Proposed Framework
Question and Answers
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Human Factors
• Input, output devices
• Interaction styles
• End-user computing
• Org. computing
• Information visualization
• Perceptual/attentive/embodied/
multi-modal/portable/wearable/
implant/personalization
• Persuasive computing
• Affective computing
Task / job
• Task goals
• Task character
• Task complexity
Context
Global Context
• National culture
• Norms
• Universal accessibility
Social Context
Use
Impact
Basic Technology
Advanced Technology
Design
Technology
• Privacy
•Trust
• Ethics
• Norms
Org. Context
Human
Demographics
• Gender, age, culture
• Comp. experience
• Education
Physical/Motor
• Motor control
• Comfort
Cognition
• Cognitive style
• Perception
• Attention
• Memory
• Knowledge
• Learning
• Error
• Distributed cognition
Emotion &
Motivation
• Affectivity
• Affective state
• Mood/feeling
• Emotion
• Intrinsic motivation
• Extrinsic motivation
• Org. goals
• Org. culture & norms
• Policy & procedures
• Management support
Group Context
• Group goals
• Group norms
Source: Zhang and Li Review of Intellectual Development of HCI Research, 2005
(13 yrs of articles in 7 top journal – 348 articles)
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Agenda
Introduction and Background
Optimal Foraging Theory (OFT)
Foraging Models
Economic perspective (EMH and REH)
Human Factors
Proposed Framework
Question and Answers
14
Constructs
The task of the seller is to minimize the consumer surplus, while the task
of the consumer is to maximize it. It is important to note that if there are
no consumer surplus, the sale cannot occur (consumers would be
unwilling to proceed).
Therefore some consumer surplus has to exist, however marginal. In a
foraging model an implicit equilibrium should exists between the
consumer price, the foraging costs and the foraging surplus on one side
and the optimal (best price available) and the incremental foraging costs
of locating this best price.
CP  FC  S  OPT  FC  FC
Or simplified:
CP  S  OPT  FC
CP = Cost of item (paid)
FC = Foraging costs
S
= Foraging surplus
OPT = Optimal price available (all patches)
∆FC = Additional foraging costs required to locate optimal price
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Constructs
CP  FC  S  OPT  FC  FC
S  OPT  FC  CP  FC
CP = Cost of item (paid)
FC = Foraging costs
S
= Foraging surplus
OPT = Optimal price available (all patches)
∆FC = Additional foraging costs required to locate optimal price
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Time Constructs
ID
TA
Variable
Time to
Access
TO
Time to
Orient
TE
Time
Enter
TF
Time to
Find
TR
Time to
Review
TZ
Time to
Acquire
Definition
The time it takes
before the patch
becomes accessible
Time it takes from
entering a patch to
become informed
of its purpose and
content
The time it takes to
enter all the
required search
criteria to search
products or
services at a patch.
Time to execute
search at a patch
Calculation
Measured as time from
last action until the site is
available
Measured from first
availability of the site to
the next action is
undertaken
Example
Load time of www.delta.com
airline web site
Measured as time from
beginning entering a
search at a patch until
action is submitted.
Time it takes to enter a search
for a flight between two cities at
a given day and for a coach
ticket.
Measured from the search
action is submitted until
the complete set of
options are available
Time it takes for Delta's web site
to execute the search and present
the results (i.e. 10 possible
flights).
Time it takes to
review the items
available at a patch
after search has
been completed.
Time it takes to
take possession of
an item
Measured from the time a
result set has been
presented until next non
review action is taken
Time it takes a customer to
review the 10 flights and pick
the best option, try another
search, or leave the website.
Measured as the time an
item has been identified
as the solution, until
ownership has transferred
The time it takes to select the
flight, enter the passenger name,
credit card and other data to the
purchase confirmation is
received.
Time from the web site is loaded
until customer undertakes an
action
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Foraging Costs and Incremental Foraging Costs
p
FC   E (tai  toi  tsi  tzi )
i 1
 p

FC    E (tai  toi  tsi  tzi ) 
 i p

E = cost per unit of time
p = number of patches accessed
ta = Time to access patch
to = Time to orient at patch
ts = Time to search
tz = Time to acquire item
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Foraging Search Costs
s
qx
x 1
j 1
ts   (tex  tf x   trx , j )
s = Number of searches at a patch
te = Time to enter search
tf = Time to find (execute search)
q = Number of items returned for review
tr = Time to review each item
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Overall model (work in progress)
sk , i
qk ,i , x
pk







S   OPTk  FCk  CPk   E tak ,i  tok ,i    tek ,i , x  tf k ,i , x   trk ,i , x, j   tz k 


k 1 
i 1
x

1
j

1






n
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Example - calculations
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Some hypothesis
HYPOTHESIS 1:
Surrender events increases foraging surplus of participants
(surrender benefits)
in electronic commerce.
HYPOTHESIS 2:
As the number of available e-commerce sites for a given
(specialization)
product increases, usage consolidates to a few sites.
HYPOTHESIS 3:
A high number of items returned in a given search has a
(information overload)
negative influence on the foraging surplus realized by
actors in an e-commerce marketplace and the impact is not
uniform for all participants.
HYPOTHESIS 4:
A negative relationship exists between foraging surplus and
(item availability)
a low number of items returned by a search.
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Some hypothesis
HYPOTHESIS 5:
The process of site exhaustion (SE) reduces radical changes
(site exhaustion)
in patch choices and is inversely related to previous
experience.
HYPOTHESIS 6a:
Actors that exhibits a moderate propensity to explore
(exploration)
increase their foraging surplus.
HYPOTHESIS 6b:
A high propensity to explore is negatively related to
(exploration)
foraging surplus (s).
HYPOTHESIS 6c:
Age is a factor in the actor’s propensity to explore.
(age - exploration)
HYPOTHESIS 6d:
The propensity to explore is directly related to previous
(experience - exploration)
experience.
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Your Turn!
How to contact me:
Bjarne Berg
bergb@lr.edu
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