Information foraging 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 for information, humans use "built-in" foraging mechanisms that evolved to help our animal ancestors find food. Importantly, a better understanding of human search behavior can improve the usability of websites or any other user interface.
🌿 Foraging in High-Dimensional Data
My talk from the Diverse Intelligences Summer Institute 2025 on how we can draw from thinking in complex systems science and game design to heal our relationship to the Web.
Misreading as Foraging: How Systems Get Used for Things They Weren't Made For - Astral's Blog
Information Scent: How Users Decide Where to Go Next
When deciding which links to click on the web, users choose those with the highest information scent — which is a mix of cues that they get from the link label, the context in which the link is shown, and their prior experiences.
AI search answers are the fast food of your information diet – convenient and tasty, but no substitute for good nutrition
An information scientist explains that while Google’s AI Overviews and other AI search tools may look enticing, you shouldn’t rely on them to fill all your search needs.
AI search answers are the fast food of your information diet – convenient and tasty, but no substitute for good nutrition
An information scientist explains that while Google’s AI Overviews and other AI search tools may look enticing, you shouldn’t rely on them to fill all your search needs.
Search has its own bitter lesson
Human incentives to make content findable to your search matter more than technology
Platform Information Provision and Consumer Search: A Field Experiment
Despite substantial efforts to help consumers search in more intuitive ways, text search remains the predominant tool for product discovery online. In this paper, we explore the effects of visual and textual cues for search refinement on consumer search and purchasing behavior. We collaborate with one of the largest e-commerce platforms in China and study its roll out of a new search tool. When a customer searches for a general term (e.g., “headphones”), the tool suggests refined queries (e.g., “bluetooth headphones” or “noise-canceling headphones”) with the help of images and texts. The search tool was rolled out with a long-run experiment, which allows us to measure its short-run and long-run effects. We find that, although there was no immediate effect on orders or total expenditures, the search tool changed customers’ search and purchasing behavior in the long-run. Customers with access to the new tool eventually increased orders and expenditures compared to those in the control group, especially for non top-selling products. The purchase increase comes from more effective searches, rather than an increase in activity on the platform. We also find that the effect is not only driven by the direct value of suggested searches, but also by customers indirectly learning to perform more effective searches on their own.
The Law of Conservation of Information: Search Processes Only Redistribute Existing Information
Conservation of information sparked scientific interest once a recurring pattern was noticed in the evolutionary computing literature. In grappling with the creation of information through evolutionary algorithms, this literature consistently revealed that the information outputted by such algorithms always needed first to be programmed into them. Thus, the primary goal of this literature—to uncover how information could be created from scratch or de novo —was shown to be misconceived: the information was not created but instead shuffled around or smuggled in, implying that it already existed in some form or other. Information output in these situations therefore always presupposed a counterbalancing input of prior information. Once this pattern was seen, the next logical step was to quantify the amount of information inputted and outputted, demonstrating a consistent mathematical relation between the two. This led to the proof of a number of theorems about search. In these theorems, a baseline search with probability p of success gave way to an improved search with probability q of success. Typically p would be very small and close to zero, implying a practically impossible search (like searching for a needle in a haystack). By contrast, q would be much larger and close to one, implying an eminently doable search. The punchline of these theorems was that, as the improved search became itself the subject of a search (a search for a search , or S4S), the probability of finding it could not exceed p / q , rendering success of the improved search no more probable than success of the original baseline search, in effect filling one hole by digging another. Such conservation-of-information theorems, as they came to be called, were search-space specific, adapted to different kinds of search across a range of search spaces. There was a measure-theoretic theorem in which probability measures guided search. There were also function-theoretic and fitness-theoretic theorems where mappings into the search space as well as fitness functions on the search space respectively guided search. The key insight of this paper is that all these conservation-of-information theorems are special cases of a simple probabilistic relation based on elementary probability theory. This paper identifies the underlying rationale that makes all the previous conservation-of-information theorems work. In so doing, it provides a straightforward proof and general formulation of what may rightly be called the Law of Conservation of Information.
Remembering the pre-Google web, when search was an experiment
Most people have completely forgotten how chaotic it really was."
Models of collaborative tagging
Collaborative tagging, also known as social tagging or folksonomy, allows users to apply public tags to online items, typically to make those items easier for themselves or others to find later. It has been argued that these tagging systems can provide navigational cues or "way-finders" for other users to explore information.[1][2] The notion is that given that social tags are labels users create to represent topics extracted from online documents, the interpretation of these tags should allow other users to predict the contents of different documents efficiently. Social tags are arguably more important in exploratory search, in which the users may engage in iterative cycles of goal refinement and exploration of new information (as opposed to simple fact-retrievals), and interpretation of information contents by others will provide useful cues for people to discover topics that are relevant.
Food, foragers, and folklore: the role of narrative in human subsistence
Narrative is a species-typical, reliably developing, complex cognitive process whose design is unlikely to have emerged by chance. Moreover, the folklore record indicates that narrative content is consistent across widely divergent cultures. I have argued elsewhere that a storyteller may use narrative to manipulate an audience's representations of the social and/or physical environment to serve his or her own fitness ends. However, my subsequent research suggests that such manipulation results from a broader selection pressure which narrative effectively alleviates: information acquisition. By substituting verbal representations for potentially costly first-hand experience, narrative enables an individual to safely and efficiently acquire information pertinent to the pursuit of fitness in local habitats. If this hypothesis is true, narrative should be rich with information useful to the pursuit of fitness. One class of information integral to the accomplishment of this task is foraging knowledge. In this paper, then, I present evidence that foraging peoples use narrative to transmit subsistence information: specifically, I demonstrate how various narrative devices (e.g., setting, description, mimicry, anthropomorphism) are used to communicate foraging knowledge.
Google Search Is Dying. What Comes Next Is Worse | The Walrus
As AI eats the web, the internet’s collective memory is disappearing
Litter Layer
Indie search engine for discovering personal and small-web sites — built largely by visitors like you.
Google Search's guidance about AI-generated content | Google Search Central Blog | Google for Developers
In this post, we'll share more about how AI-generated content fits into our long-standing approach to show helpful content to people on Search.
Learning with AI falls short compared to old-fashioned web search
Doing the mental work of connecting the dots across multiple web queries appears to help people understand the material better compared to an AI summary.
Learning with AI falls short compared to old-fashioned web search
Doing the mental work of connecting the dots across multiple web queries appears to help people understand the material better compared to an AI summary.