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WHICH of these activities occupies more of your time: foraging for food or
surfing the Web? Probably the latter. We鈥檙e all informavores now, hunting down
and consuming data as our ancestors once sought woolly mammoths and witchetty
grubs. You may even buy your groceries online.

But in an odd sort of way, Internet shopping has brought us full circle.
According to researchers in the US, the strategies you use when you surf the Web
are exactly the same as the ones hunter-gatherers used to find food. You may be
plugged into the information superhighway, but deep down you鈥檙e still a
caveman.

At least that鈥檚 the opinion of two researchers at Xerox鈥檚 Palo Alto Research
Center in California. Peter Pirolli and Stuart Card are using foraging theories
from ecology and anthropology to understand how people find information in
data-rich environments such as the Internet. They believe Web surfers rely on
prehistoric instincts to maximise their yield when they hunt and gather morsels
of information. If they鈥檙e right, their results could help others design
websites and search tools that are as alluring to informavores as flowers are to
bees.

Biologists came up with foraging theory in the 1970s as a way of explaining
some puzzling aspects of animal behaviour. A hungry fox, for example, might have
the choice between chasing a big, juicy rabbit or a tiny vole. Which should it
choose? Foraging theory can decide. It states that as far as possible, animals
make choices that maximise their 鈥渂enefit per unit cost鈥�. In other words,
they鈥檒l expend food-gathering energy in ways that yield the best energy returns.
The rabbit might have a high energy value, but it costs a lot to catch. The vole
is much easier prey.

This cost-benefit analysis is complicated by the fact that food resources
aren鈥檛 evenly distributed around the world. They鈥檙e patchy. The longer a forager
exploits one patch, the lower the returns will be, until the patch is overgrazed
and worthless. But time spent searching for a new patch is
unprofitable鈥攖here鈥檚 nothing to gain from a sterile space. So when is the
best time to start looking for a new patch?

It turns out that the optimal strategy is to move on when the rate of return
from a particular patch falls below the average rate over the whole region. This
is the marginal value theorem, a cornerstone of foraging theory formulated by
the University of New Mexico biologist Eric Charnov in 1976. And it doesn鈥檛 just
apply to animals. The theorem has been widely used in anthropology to explain
all sorts of human behaviours, from food preferences to patterns of land
tenure.

Pirolli and Card now believe the same idea can be used to understand
information foraging. Imagine you鈥檙e a financial analyst looking for data about
an investment company. You鈥檝e found a useful site on the Web, but it鈥檚 starting
to feel a bit stale. You鈥檇 like to move on, but you know that a search will take
time and there鈥檚 no guarantee that other sites will be any more useful. When
should you abandon the dwindling supply? This, Pirolli and Card argue, is
analogous to the problem faced by hunter-gatherers. And it can be solved in the
same way.

The first inkling that this was the case came in 1992. Pirolli and Card were
studying the relationship between humans and information, looking for a theory
that explained how people performing data-intensive tasks decide where and
how to look for data. They had already conducted what they call 鈥渜uick and
dirty鈥� field studies of information-gathering behaviour, one on a group of MBA
students and another on the author of a business newsletter.

Pirolli knew something of foraging theory, and he quickly noticed a
correlation between the studies鈥� findings and the behaviour you鈥檇 expect from
animals searching for food. Like hungry foxes, information foragers try to
maximise their benefit per unit cost鈥攊n this case, 鈥渂enefit鈥� meaning the
relevance of the information and 鈥渃ost鈥� the time it takes to find it. They are
also likely to move on from an information resource when it no longer yields a
better-than-average return.

It was a satisfying analogy, but they needed empirical findings to back it
up. So they designed a computer model that obeyed the rules of optimal foraging
theory and set it to work looking for information.

The latest incarnation of Pirolli and Card鈥檚 artificial forager is based on
ACT, a theory of cognition developed by Carnegie Mellon computer scientist John
Anderson. ACT stands for both Adaptive Character of Thought and Atomic
Components of Thought, and is well suited to the research because it possesses
human-like conceptual and problem-solving skills鈥攖hings an information
forager needs in abundance. On top of these, Pirolli and Card programmed in the
rules of optimal foraging theory.

To test whether the theory produces useful results, Pirolli and Card set
their model to work looking for information on a database. The database they
chose was the IR Test Collection, one of the ultimate challenges in information
science. It鈥檚 a huge reservoir of texts from The Wall Street Journal,
the Financial Times, the San Jose Mercury-News, the Associated
Press newswire, the Department of Energy, the Federal Register, the US Patent
Office, computer publisher Ziff-Davis and a handful of sources in Japanese,
Spanish and Chinese. It contains more than a million documents.

Fastest route

Pirolli and Card pinpointed target documents in the IR Test Collection and
worked out the most efficient strategies for retrieving them. For this, they
used an information retrieval system called Scatter/Gather designed for sifting
through large databases. Scatter/Gather assigns each document to one of 10
groups according to its content, so documents that contain similar words end up
in the same group. It presents these on screen as 10 boxes, each containing a
collection of keywords. The user selects one or more of the groups.
Scatter/Gather then discards the documents in the unselected boxes and scatters
the remainder into ten more groups. It repeats the process until the user is
satisfied that it鈥檚 worth reading the gathered texts.

To find the fastest retrieval routes鈥攊n other words, those using the
smallest number of steps鈥擯irolli and Card worked backwards through
Scatter/Gather, starting from the target documents. Then they asked their
artificial forager to go find the same pieces of information within the IR Test
Collection. It did so with little problem. When Pirolli and Card plotted the
forager鈥檚 track through the collection, it matched the ideal route almost
perfectly.

They then recruited eight human volunteers and asked them to perform the same
task. Again, their routes closely matched the ideal one. It seems as though
informavores really do employ optimal foraging strategies to sniff out rich
information patches and avoid the arid plains.

Experts in foraging theory agree. 鈥淚t鈥檚 likely Web users rely on
problem-solving abilities with deep evolutionary roots,鈥� says Bruce
Winterhalder, an anthropologist at the University of North Carolina at Chapel
Hill who has studied human foragers in great detail. 鈥淔oraging on the Web
presents trade-offs analogous to those of hunter-gatherers. Different context,
but similar cost-benefit problems.鈥� Biologist David Stephens of the University
of Minnesota, who co-wrote the seminal 1986 book Foraging Theory, adds:
鈥淎nimals have been solving search problems for millennia, and natural selection
has made them good at it. It follows that we can learn something from them.鈥�

What that means in practical terms is that database and Web designers could
use foraging theory to help them create more productive information resources.
The theory could prove particularly useful at that crucial moment when a forager
starts thinking about leaving one patch in search of another.

In this respect, one of the most useful ideas the research has produced is
that of 鈥渋nformation scent鈥�. Pirolli and Card guessed that information
foragers鈥攚hether human or artificial鈥攈ave some way of evaluating the
likelihood of finding target information in a given Scatter/Gather box. This led
them to the idea that associated concepts 鈥渞ub off鈥� on one another, leaving
detectable traces, just as a watering hole frequented by woolly mammoths will
smell of woolly mammoths. A hunter-gatherer seeking mammoths is likely to be
drawn to the watering hole, if only to look for spoor. Information foragers do
the same. Imagine you鈥檙e looking for texts about foraging theory. If
Scatter/Gather throws up a box containing the keyword 鈥渉unter-gatherer鈥�,
you鈥檙e likely to select that box. It just smells right.

Xerox is now trying to capture the essence of information scent and infuse it
into Web pages, giving surfers subtle come-ons as they sniff around for useful
sites. 鈥淲e are developing technologies that help designers make page layouts
that give off good information scent鈥攃ues that allow users to assess the
match of information to their needs and identify how to get to it,鈥� says
Pirolli.

The analogy between food and information looks like being a big help to Web
designers. But at some point, Pirolli says, it鈥檚 likely to break down. For one
thing, there鈥檚 the question of evaluating costs and benefits. Biologists and
anthropologists can always draw up an energy balance sheet for a foraging
behaviour in joules. The value of information isn鈥檛 so easy to measure. Another
problem is that foraging models tend to assume environments stay the same over
time, whereas information ecologies are nothing if not dynamic. Ingenious
informavores鈥攁nd those who seek to provide them with information鈥攃an
actively manipulate their environment.

And even if information foraging theory works, there鈥檚 no guarantee that it
will be used to benefit the forager. Think of insectivorous flowers that lure
flies with the scent of carrion. As Card points out: 鈥淭he vendor鈥檚 interests may
not correspond with the searcher鈥檚. They may camouflage information to hide it
or mimic something that they think you want. Banner ads, especially ones with
fake buttons on them, are an example.鈥� So next time you鈥檙e hunting down
information on the Web, beware. It could smell like a juicy rabbit, but turn out
to be a vole.

Peter Pirolli and Stuart Card aren鈥檛 the only ones looking at the way
prehistoric adaptations shape the way we handle information. Pamela Sandstrom, a
librarian at the Helmke Library of Indiana University-Purdue University, Fort
Wayne, is using foraging theory to guide scholars into promising new academic
territories.

Sandstrom uses a technique called co-citation analysis to map clusters of
researchers with similar research interests. Authors who are frequently found
side by side in a bibliography will crowd together in her three-dimensional
information space, while authors who are seldom or never co-cited will stand
apart.

Scholars tend to graze the patches closest to the core of their discipline,
where the costs of locating useful information are very low. 鈥淎s foragers,鈥� says
Sandstrom, 鈥渨e鈥檙e definitely trying to maximise rewards.鈥�

Problem is, these easily accessed patches are likely to be overgrazed. And
while it鈥檚 in scholars鈥� interests to explore virgin territory, finding useful
new patches is costly. You need to employ labour-intensive search strategies to
forage successfully in interdisciplinary areas.

To help reduce the risks of foraging in unfamiliar territory, Sandstrom
proposes a scale that ranks the relative novelty of each information patch. This
could help researchers find clusters that are related to their own, but are
little known by their colleagues. In this way, Sandstrom hopes to increase the
benefit per unit cost of foraging in potentially fruitful patches.