Peter Pirolli and Stuart Card [Pirolli and Card, 1995] propose a theory of Information Foraging that applies the ideas from optimal foraging theory - that has been developed within biology for understanding the opportunities and forces of adaptation and in which humans are seen as adapters - to understand how human users adapt strategies and technologies for information seeking, gathering, and consumption to their environments and to the context of the information on a situational basis to yield higher gains and maximize gains of valuable information per unit cost . Information Foraging theory analyzes how these adaptations trade-off the value of information gained against the costs of performing the activity in human-computer interaction tasks.
This theory shows that information seekers use the same strategies as food foragers: Like food foragers, information foragers rely on ”cues” in the environment that help seekers judge whether to continue foraging in the same location or to forage elsewhere. For example, web searchers examine the images and text to determine whether the site has what they’re looking for. These cues are called Information scent which is a component of the Information Foraging concept. And just like a food forager, information foragers use a cost-benefit analysis where the benefit is the information they seek and the cost is the time it takes to find it. And once the costs of the current ”information patch” outweigh its remaining benefits, they move on to a different Web site or database. Human users estimate how much useful information are they likely to get on a given path and then compare the efforts with the expected outcome.
Consequently, elements of this theory can help in understanding existing human adaptations for gaining and making sense out of information. It can also give researcher of user interaction with Web sites a way to examine user goals, their decision making processes and adaptations to the information access system environment. Researchers can then make use of this knowledge in assessing system and interface design. Better understanding of human search behaviour can improve the usability of websites or any other user interface layout. Providing people with access to more information is not the problem. Rather, for Information Foraging theory, central problems in information gathering and sense making are to facilitate finding and collecting information, to maximize the allocation of human attention to information that will be useful, and to optimize the seekers time.
Key dimensions to this analogy include:
- Information scent: Predicting a path's success
- Does your page navigation signal to the user that the have reached, or are nearing their goal?
- Does the destination page meet the expectations set by the navigation?
- Diet selection: What to eat
- Does your site provide 'nutritious' information?
- How easily can a user find and use this information?
- Patch selection: When to hunt elsewhere
- Does you site address users immediate needs?
As a result of these considerations, information should be designed to help people determine if they’ve exhausted the supply of information (e.g. by clearly indicating the scope of a website) and should provide opportunities for serendipitous discovery.
Comments by Paraschos Zeimpekos and Rawia Awadallah (November 2004)
Information Foraging theory in [Wikipedia, 2004] is summerized as follows : It is a theory that applies the ideas from optimal foraging theory to understand how human users search for information. The theory is based on the assumption that, when searching, humans utilize ”built-in” foraging mechanisms that evolved to help our animal ancestors find food. Better understanding of human search behaviour can improve the usability of websites or any other user interface layout. The most important concept in the Information Foraging theory is ”information scent”. Like animals rely on scents to indicate the chances of finding prey in current area and guide them to other promising patches, humans rely on various cues in the information environment to get similar answers. Human users estimate how much useful information are they likely to get on a given path and then compare the efforts with the expected outcome. When the information scent stops getting stronger, people move to a different information source.
Information Foraging Theory in [Pirolli and Card, 1999] is defined as an approach to understanding how strategies and technologies for information seeking, gathering, and consumption are adapted to the flux of information in the environment. The theory assumes that people, when possible, will modify their strategies or the structure of the environment to maximize their rate of gaining valuable information. Field studies inform the theory by illustrating that people do freely structure their environments and their strategies to yield higher gains in Information Foraging. The basic hypothesis of Information Foraging Theory is that, when feasible, natural information systems evolve towards stable states that maximize gains of valuable information per unit cost .
[Pirolli and Card, 1999]
In [Pirolli and Card, 1999], the theory is developed by:
- adaptation (rational) analysis of Information Foraging problems which develops
- information patch models, which deal with time allocation and information filtering and enrichment activities in environments in which information is encountered in clusters (e.g., bibliographic collections),
- information scent models which address the identification of information value from proximal cues, and
- information diet models which address decisions about the selection and pursuit of information items.
- a detailed process model (ACT-IF) which developed to instantiate these rational models and to fit the moment-by-moment behavior of people interacting with complex information technology. ACT-IF is a production system in which the information scent of bibliographic stimuli is calculated by spreading activation mechanisms. Time allocation and item selection heuristics make use of information scent to select production rules in ways that maximize Information Foraging activities.
From [Pirolli and Card, 1999] point of view, providing people with access to more information is not a problem. Rather, the problem is one of maximizing the allocation of human attention to information that will be useful, For Information Foraging Theory, a central problem in information gathering and sensemaking is the allocation of attention. Information Foraging Theory could also provide the scientific basis from which we might engineer new innovations that enrich the information that people process.
In [Rabourn, 2002] it is mentioned that Information Foraging theory seeks to explain information-seeking behavior in humans. Central to its thesis is that Information Foraging is an exaptation of food foraging mechanisms, therefore models of optimal foraging theory developed by anthropologists and ecologists in the study of food foraging will help understand foraging behavior in consumers of information. These models allow us to investigate foraging behavior in relation to particular environmental conditions and the constraints of foraging for information in a dynamic ecology.
Moreover, [Rabourn, 2002] says that Information Foraging theory gives those researching user interaction with Web sites a way to examine user goals, their decision making processes and adaptations to the information access system environment. Researchers can then make use of this knowledge in assessing system and interface design. Most importantly to those charged with developing a web site, Information Foraging theory can then inform design.
In addition, [Rabourn, 2002] demonstrates and gives examples of ways web developers can use Information Foraging theory to cultivate more attractive paths to richer patches of information on a web site by knowing their visitors’ information diets, allowing users to take advantage of the paths created by others, and providing representations of content with a strong information scent.
By [Nielsen, 2003] Information Foraging , is believed to be the most important concept to emerge from Human-Computer Interaction research since 1993. Developed at the Palo Alto Research Center (previously Xerox PARC) by Stuart Card, Peter Pirolli, and colleagues, Information Foraging uses the analogy of wild animals gathering food to analyze how humans collect information online.
[Nielsen, 2003] says that Web users behave like wild beasts in the jungle people like to get maximum benefit for minimum effort.The thing makes Information Foraging a useful tool for analyzing online media.
[Nielsen, 2003] talked a bout baisc principles in Informatiom Foraging and how can they benifit web design.
[Pirolli and Card, 1995]
In [Pirolli and Card, 1995], Information Foraging theory is an approach to the analysis of human activities involving information access technologies. The theory derives from optimal foraging theory in biology and anthropology, which analyzes the adaptive value of food-foraging strategies. Information Foraging theory analyzes trade-offs in the value of information gained against the costs of performing activity in human-computer interaction tasks. Information Foraging theory. This approach considers the adaptiveness of human-system designs in the context of the information ecologies in which tasks are performed. Typically, this involves understanding the variations in activity afforded by some space of human-system design parameters, and understanding how these variations trade-off the value of information gained against the costs of performing the activity. Information Foraging theory emphasizes a larger time-scale of behavior, the cost structure of external information- bearing environments, and human adaptation.
In [Pirolli and Card, 1995], a brief idea about Optimal foraging theory is given . It is said to be a theory that has been developed within biology for understanding the opportunities and forces of adaptation. We believe elements of this theory can help in understanding existing human adaptations for gaining and making sense out of information. It can also help in task analysis for understanding how to create new interactive information system designs.
Optimality models in general include the following three ma jor components.
- Decision assumptions that identify which of the problems faced by an agent are to be analyzed. This might involve decisions about whether to pursue a given type of information item upon encountering it, the time to spend processing a collection of information items, the choice of moves to make in navigation, the choice of strategy under uncertainty, or degree of resource sharing.
- Currency assumptions, which identify how choices are to be evaluated. The general assumption in ecological analyses is that some feature x will exist over other features, if x satisfies some existence criteria. Existence criteria have two parts a currency, and a choice principle. Typically, optimal foraging models in anthropology and biology assume energy as a currency. Information Foraging theory will assume information value as currency. Choice principles include maximization, minimization, and stability.
- Constraint assumptions, which limit and define the relationships among decision and currency variables. These will include constraints that arise out of the task structure, interface technology, and the abilities and knowledge of a user population.
[McFedries, 2004] says that Information Foraging theory views humans as informavores, continually seeking information from our environment. In a sense we are foraging for information, a process with parallels to how animals forage for food. For both human and animal there are cues in the environment that help us judge whether to continue foraging in the same location or to forage elsewhere.
In [McFedries, 2004] the following facts are mentioned :In the early 1990s, Peter Pirolli and Stuart Card of Xerox’s Palo Alto Research Center observed that tracking down information was analogous to foraging for food, so they tried applying foraging theory to information hunting and gathering. Their results, which were first presented in a paper at the 1995 Conference on Human Factors in Computing Systems, showed that information seekers do use the same strategies as food foragers. That is, they use a cost-benefit analysis, just like a food forager, where the benefit is the information they seek and the cost is the time it takes to find it. And once the costs of the current ”information patch” outweigh its remaining benefits, they move on to a different Web site or database. Also, like food foragers, information foragers rely on ”cues” that tell them whether a particular patch contains the data they seek.
Moreover, in [McFedries, 2004] Information scent is said to be a component of the Information Foraging concept. When animals are foraging for food, they often use scent to determine whether a particular area is worth investigating. Hunters, for example, will sniff around for evidence that prey has been in the area.  believes that Web searchers do something similar. When they first arrive at a site, they examine the images and text to determine whether the site has what they’re looking for. Someone looking for device drivers, for example, will hunt for a link labeled ”Downloads” or, even better, ”Device Drivers.” Labels such as ”Products” and ”Purchase” aren’t as promising that is, they don’t give off a good information scent.
[Kalbach, 2000] says the following: Peter Pirolli and Stuart Card (1995) propose a theory of Information Foraging as an approach to analyzing human activities involving information access technologies. This theory is directly based on foraging theories in biology and anthropology in which humans are seen as adapters. The theory analyzes the trade-offs in the value of information gained against the cost of performing a task necessary to find information. It is important to note that foraging for information does not equate to aimless ”surfing.” Foraging refers to the variety of strategies seekers exhibit in their quest for information and how humans adapt to their environments on a situational basis. Consequently, in an information-rich world the real design challenges are not only how to facilitate finding and collecting information, but how to optimize the seekers time.
[Sarkar and Brown, 1992]
In [Sarkar and Brown, 1992], the following is mentioned :Based on optimal foraging theory, Information Foraging theory draws parallels between the complex behaviors associated with foraging activities in the wild and Information Foraging choices in information environments . In both cases, the goal of the forager is to maximize the utility returned per unit of effort. Common to other optimization models, optimal foraging theory includes an actor and three factors: a strategy set, currency, and constraints. A strategy set specifies all of the choices or decisions available to an actor at any point in time. Currency includes the costs and benefits a decision or activity including the activities associated with the actual expenditures of time and energy such as tracking, pursuing, and consuming the prey. Foraging costs also include opportunity costs (the benefits that could have been achieved by engaging in other activities, but forfeited by engaging the chose activity). Constraints include limits to foraging activities beyond the actors control. These constraints include both intrinsic constraints (strength, skills, etc.) and extrinsic constraints (terrain, weather conditions, etc.).
- [Kalbach, 2000] James Kalbach, Designing for information foragers: A behavioral model for information seeking on the world wide web, internetworking 3.3, Created at: December 2000. Retrieved at: November 19, 2004, http://www.internettg.org/newsletter/dec00/article_information_foragers.html
- [McFedries, 2004] Paul McFedries. Word spy - information foraging, Created at: December 19, 2002. Retrieved at: November 19, 2004. http://www.wordspy.com/words/informationforaging
- [Motive, 2004] Motive, The Motive Internet Glossary. Created at: October 22, 2004. Retrieved at: November 2004. http://www.motive.co.nz/glossary/information-foraging.php
- [Nielsen, 2003] Jakob Nielsen, Information Foraging: Why Google Makes People Leave Your Site Faster, Alertbox, June 30, 2003. Created at: June 30, 2003. Retrieved at: November 2004. http://www.useit.com/alertbox/20030630.html
- [Pirolli and Card, 1995] Peter Pirolli and Stuart Card. Information foraging in information access environments. Proceedings of ACM Conference on Human Factors in Computing Systems (CHI '95), 1995.
- [Pirolli and Card, 1999] Peter Pirolli and Stuart Card, Information Foraging, Psychological Review 106(4): 643-675, 1999.
- [Rabourn, 2002] Tanya Rabourn, Cognitive Models for Web Design - Information Foraging Theory Applied. Created at: May 2002. Retrieved at: November 2004. http://www.pixelcharmer.com/essays/information-foraging.html
- [Sarkar and Brown, 1992] Manojit Sarkar and Marc H. Brown. Graphical fisheye views of graphs. In Penny Bauersfeld, John Bennett, and Gene Lynch (editors), Human Factors in Computing Systems, CHI’92 Conference Proceedings: Striking A Balance, pages 83–91. ACM Press, Mai 1992.
- [Usability First, 2004] usability first, Usability Glossary: information foraging. Retrieved at: November 2004. http://www.usabilityfirst.com/glossary/term_1068.txl
- [Wikipedia, 2004]: Wikipedia, Information Foraging, Retrieved at: November 2004. http://en.wikipedia.org/wiki/Information_foraging