1. Overview of Information Foraging
Foraging is adapted from ecology
How people adapt themselves to the information environment
Applies to tasks such as collection background documentation for a report
Access is determined as a tradeoff of profitability and current rate of gain.
No explicit parameter for motivation (need)

2. Motivation Strategy Mathematical treatment of access issues
Model based on cognitive processes
Perhaps a paradigm shift
Strategy
Pilot experiments to show enrichment
Develop Production System model
Test with data from Scatter-Gather

3. Try to apply Ecological Foraging to Information Foraging
Foraging is well established (but controversial) in ecology
Works well for stable information seeking environments
What to do with enrichment

4. Optimization/Economic Models
Believe that organisms/individuals will optimize something
Actual behavior is some approximation to the optimal behavior (e.g., people are rational actors)
Can these models be invalidated?
GOMS / Keystroke are examples

5. Patches and Diets Patches (A place where information is concentrated)
Opportunity costs
Diets (What do you chose to consume)
What food/information will you consume
Some ambiguity about analogy of what is a patch for information
Consume if profitable relative the options.

6. Rate of Information Gain

7. Short Experiments
Knowledge Crystalization

8. Enrichment
Foraging model can be adapted so between-patch time can be invested in enrichment

9. Scent How do we know where to look for information
Used to assess profitability of different information resources
Perceptual/cognitive model based on ``proximal cues’’
Has also been used or predicting hyperbolic tree look ahead.
Compare to DIEs/Criteria/Values model (Wang and Soergel)

10. Spreading Activation Model of Scent
Estimate relationships between individual words and relevant documents
Add up words in the titles to get estimates of what user knows of where to look for relevant documents.

11. ACT_IF Model Production system model, based on ACT_R of John Anderson
Dynamic version of inferences during information foraging
Spreading activation for estimates of relevance based on scent

12. Scatter-Gather
Predicting optimal access to relevant documents

13. Scatter-Gather Experiment
TREC Collection
Two conditions (speed/ratings)
Ratings confirm scent predictions
Three tasks (easy/medium/hard)
Pick several clusters for easy task
Pick only one cluster for hard task
(contribution of additional cluster is low)

14. Fitting Specific Predictions
What is the optimal number of choices for easy-medium-hard distinctions
Differences in select/unselect as it predicts profitability and rate of information gain.
There is a sharp boundary just where model says it should be. Are you convinced?

15. Extensions Styles of Foraging: Finding a niche Foraging in groups
Sit and wait vs. Active predator
Foraging in groups
Modeling overlapping information patches
Can this apply to more complex environments? (e.g., use of search engines)

16. Implications
How many free parameters – is it surprising there was a fit to these data?
Is it useful or just descriptive?
Could we use it to design new information interfaces?
Can we engineer information environments with this model? Find optimal number of niches?
Is this an interesting approach to Information Science?
Is it an important approach?