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)

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

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

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

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.

Rate of Information Gain

Short Experiments Knowledge Crystalization

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

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)

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.

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

Scatter-Gather Predicting optimal access to relevant documents

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)

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?

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)

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?