Swarm Intelligence – solving complex problems
through social interactions
Part of:
Inquiry Science with Dartmouth
Developed by: Zak Gezon (with Melissa Fellows and Becky Irwin)
Overview
Ants are famous for being able to solve complex problems, but if you watch an individual ant it
is remarkable how bumbling and unskilled it may seem – ants get lost, that wander around at
random, and they will sometimes follow each other in circles until they starve to death, for
example. So how do they collectively solve complex problems like finding the shortest distances
between food sources and home? They do it by using swarm intelligence – the solving of
complex cognitive problems by pooling the knowledge of numerous individuals. In this lesson
we will learn about swarm intelligence, where it appears in nature, and even about how humans
unknowingly use swarm intelligence on a day-to-day basis.
Science Standards
Science process skills
S:SPS1:6:1.2 Plan observations based on a given purpose.
S:SPS1:6:1.8 Ask questions about relationships between and among observations.
S:SPS1:6:4.5 Draw appropriate conclusions based on data collected.
S:SPS1:8:4.2 Identify sources of error in experiments.
S:SPS1:8:4.3 Draw appropriate conclusions regarding the scientific question under investigation, based
on the data collected.
S:SPS3:8:1.1 Work effectively within a cooperative group setting, accepting and executing assigned
roles and responsibilities.
S:SPS3:8:1.2 Work collectively within a group toward a common goal.
S:SPS3:8:1.3 Demonstrate respect of one another’s abilities and contributions to the group.
Life science
S:LS1:8:1.2 Describe or compare how different organisms have mechanisms that work in a coordinated
way to obtain energy, grow, move, respond, provide defense, enable reproduction, or maintain internal
balance (e.g., cells, tissues, organs and systems).
S:LS2:8:1.2 Explain that in all environments, organisms with similar needs may compete with one
another for resources, including food, space, water, air, and shelter, and that in any
particular environment the growth and survival of organisms depend on the physical conditions.
S:LS2:6:3.3 Explain how insects and various other organisms depend on dead plant and animal matter for
food; and describe how this process contributes to the system. S:LS4:6:1.1 Recognize that learning
requires more than just storage and retrieval of information and that prior knowledge needs to be tapped
in order to make sense out of new experiences or information.
S:LS4:8:1.1 Recognize that unlike human beings, behavior in insects and many other species is
determined almost entirely by biological inheritance.
Focus Question
How can groups solve complex cognitive problems that would be difficult or impossible to solve
by isolated individuals?
Objectives
After learning about swarm intelligence and playing several rounds of the games, the students
will be able to:
 Explain what swarm intelligence is and where it appears in nature.
 Describe how bees use swarm intelligence to democratically choose a new home.
 Make predictions about what types of questions can be answered through swarm intelligence.
Background
At a 1906 country fair in Plymouth in the UK, 800 people participated in a contest to estimate
the weight of an ox. Statistician Francis Galton observed that the median guess, 1207 pounds,
was accurate within 1% of the true weight of 1198 pounds. He found that the mean or median
guess was far better, in fact that most individual guesses, and that the crowd somehow had a
particular wisdom that each individual member of the crowd lacked. This is swarm intelligence
– the solving of complex cognitive problems by pooling the knowledge of numerous individuals.
Swarm intelligence is used by ants to find the shortest route from their nest to their food source,
by bees to democratically vote on their new nesting site, and by people to determine their next
president. Swarm intelligence is also quite important in modern computing, and is used to
determine what search results you are most probably interested in by google, and what stories
you are mostly likely interested in on facebook. In this lesson we will learn about what swarm
intelligence is, how it works, when it works … and when it doesn’t.
Materials (for 18 students)
 4 clear plastic cups
 12 plastic baggies
 3, 16 oz. bags of large lima beans or jelly beans.
 Four folders or pieces of paper to be used as partitions.
Preparation – For this module you will need to place known numbers of lima beans into the
plastic cups so that participants can guess how many beans are in each cup. The module
goes the most smoothly if the beans have been counted out in advance. It is best to count
out and place lima beans into labeled bags. In separate, labeled bags place 50, 60, 70, 80,
90, 95, 100, 105, 110, 120, 130, 210 lima beans.
Procedure – This is what you will do during class. Time estimates for each section are included
1. Hook: Have participants guess how many grains of rice are in a full glass. Have each
student write down their guess without consulting one another. Collect the pieces of paper,
and once all guesses are in, read them aloud and have a scribe record them. Then calculate
the mean of the guesses. Reveal the actual number of beans. Compare the guesses and the
mean to the true value. The mean will probably be closer than any individual guess. ~10
minutes.
2. Background: Introduce swarm intelligence. ~10 minutes
3. Hook part 2: Do the rice experiment again with a different quantity of rice. This time have
each person say their guess out loud (thus introducing bias). Now how did they do?
Probably less well. Because they didn’t actually utilize swarm intelligence here. When
individuals independently acquire information and the information is combined and
processed through social interaction, a cognitive problem can be solved in a way that would
have been difficult or impossible to implement by isolated individuals.
4. Experiment 1: How to bees pick a new home? Background: when bees pick a new home, tens
of thousands of bees must unanimously and democratically select a new nesting site. Since it
would be inefficient to have each bee visit all possible nesting sites, each bee visits only one
potential site and then dances to communicate how much they like the site and where it is. If
other bees are convinced by the dance, they explore the site. Eventually all bees will have
“voted” on a particular site, and the whole colony moves. Bees have very particular needs for
a new nesting site, and their methods allow them to efficiently select the best possible site.
Let’s try out their methods to see if we can find the best “home.” Have 4 jars with jelly
beans. One has 100, one has 120, one has 80, and one has 60. The jars are hidden from
view, one in each corner of the room. The goal is for the students to act as bees and
investigate the jars to determine which one will make the best new “home.” The ideal jar has
100 beans. Have people explore the jars, but they may only visit a single jar (they can only
leave the colony once). When they come back to the hive they report their information by
walking in the direction of the jar and snapping more or less enthusiastically about how
much they like the jar. Then people can follow the lead and decide if they want to snap or
not. Once all jars have been explored and people have cycled through, there should be a
point when only one jar is promoted. This is declared the winner. Try it again with different
numbers of beans, making the game harder and harder each time.
5. Assessment and recap of Experiment 1: Do they tend to pick the right new home? Have them
discuss the process. Is the method more effective than everybody looking at each one and
voting? What about efficiency?
6. Experiment 2: background: swarm intelligence doesn’t work for all problems. Let’s try to
solve a different sort of problem with swarm intelligence. Ask people to write down the
answer to the following question: When flipping a coin, the probability of getting heads is
½. The probability of getting heads twice in a row is ¼. How many heads in a row would
you need to get to have the probability be equal to that of winning the NH jackpot lottery, 1
in 4.5 million?
7. Assessment and recap of Experiment 2: The actual answer is 22. How did they do? People
probably guessed WAY more than this. Have them discuss why it didn’t work. There are
some questions that even swarm intelligence isn’t good at answering. In this case, we are so
bad at estimating probability that it is a lost cause. There is a systemic bias, which renders
this problem outside of the realm where swarm intelligence works.
8. Synthesis: So what types of questions are good for answering through swarm intelligence?
Here are the qualities:
1) Ability to judge a problem without systemic bias. In the coin toss problem, we have
an innate inability to estimate probability, which is why it doesn’t work.
2) Independent information – each individual gets their information completely
independently.
3) Diversity of approaches – the more approaches that are utilized to get the answer, the
better.
4) Honesty -- Cheating screws it all up.
9. In small groups, have the participants try to come up with their own experiments that can be
solved through swarm intelligence.
Assessment
 Hook: was the mean guess closer than most individual guesses?
 Experiment 1: were they able to pick the best home most of the time?
 Experiment 2: was the mean guess closer than most individual guesses?
 Synthesis Could they design questions that could be answered through swarm intelligence?
Extensions
If time permits, lead a discussion about swarm intelligence. What are some common applications of
swarm intelligence? Democratic elections? Crowd funding? Reddit’s up-vote/down-vote system?
When does swarm intelligence go wrong? Can it be used to manipulate us? In an extension of experiment
2, have one member of the crowd secretly try to guide the group to a particular corner of the space. They
will probably be able to do so easily.
Swarm Intelligence
name ___________________ date________
How many grains of rice are in the cup? Take your best guess: ___________________________
What was the mean guess? _________________ What was the true value? ______________________
What about the second try? Your best guess: ___________________________
What was the mean guess? _________________ What was the true value? ______________________
How did your guess compare to the true value? ______________________________________________
How did the mean guess compare to the true value?___________________________________________
Experiment 1: Did you find the right home? ____ What about in subsequent trials? ____ ____ ____
Experiment 2: When flipping a coin, the probability of getting heads is ½. The probability of
getting heads twice in a row is ¼. How many heads in a row would you need to get to have the
probability be equal to that of winning the NH jackpot lottery? ________________
What was the mean guess? _____ What was the true value? _____Was is close? _________
What types of questions are good for answering through swarm intelligence?
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Design your own experiment that could be solved through swarm intelligence! Describe it below
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