4.2.1 Quorum Decisions as Swarm Problem Solving among Animals

In animals’ collective decision-making, quorum decisions will rely on independent assessments in the first phase of the process. When a specific threshold is met, there will be a distinct behavioral shift in mode towards dependent behavior. Everyone will copy the preferred behavior. Most importantly, both the speed and accuracy of decision-making can be improved by copying the choice of a better-informed neighbor (Sumpter, Reference Sumpter2010). Quorum decisions ensure that a minimum number of individuals (the actual quorum number) are ready to shift from one behavior to the next. Because decisions taken by several individuals are generally more accurate than individual decisions made alone, quorum thresholds reduce the risk of errors (Bousquet, Sumpter, & Manser, Reference Bousquet, Sumpter and Manser2011).

This behavior has primarily been studied in honeybees, ants, and fish (Bousquet et al., Reference Bousquet, Sumpter and Manser2011). However, there are differences, as ants use tandem runs as recruitment signals, while bees use dances (Figure 4.1). Still, there are also strong similarities between the decision processes of Temnothorax ants, honeybees, and even cockroaches since all three species exhibit positive feedback and quorum responses. Because decision-making in animal groups often will be decentralized, positive feedback plays an important role. A plausible explanation is the evolutionary consequence of a need by individuals to reach consensus (Sumpter, Reference Sumpter2010).

Figure 4.1

Two worker ants of the species Temnothorax albipennis performing a tandem run.

image courtesy of Thomas O’Shea-Wheller, 2016

In one experiment, small groups of fish had to swim through a Y-shaped maze where replica conspecifics were set up down both sides of the maze. Interestingly, smaller groups of one or two fish were more likely to be influenced by the replicas than larger groups of four or eight fish. If the difference between the number of replicas moving to each side was only one (e.g., if left:right was 1:0 or 2:1), the larger groups were not influenced by the majority at all. However, if the difference in replicas was two (e.g., if left:right was 2:0 or 3:1), the larger groups were much more likely to follow the majority. The results show that fish only follow a certain majority size (response threshold), and they are able to compare their own group size with the numbers of fish in their surroundings (Sumpter, Reference Sumpter2010).

In another experiment on a potentially dangerous situation, groups of four or eight fish only swam past a predator replica when guided by two or more “leader” replicas, while they usually ignored the behavior of one single “leader” replica. However, a single fish who would never swim past a predator alone would still do it sometimes if led by a single “leader” replica. The results show that uncommitted individuals in larger groups only follow above a threshold number of leaders. This threshold dramatically reduces the probability of errors being amplified because if the probability of one individual making an error is small, the probability that two fish independently make the same error simultaneously is very small. Interestingly, experiments show that humans also ask for the opinions of two other individuals if they want to be more certain about a particular choice. The quorum rule of following more than one leader allow both fish and humans to make more accurate decisions as group size increases (Sumpter, Reference Sumpter2010).

Another example is Temnothorax ants who live in colonies of between 50 and 500 individuals in small rock or wood cavities. If their nest is damaged, they are able to move to a new site within a few hours, and will nearly always choose the best site from as many as five alternatives. They are able to assess new sites from several environmental cues such as cavity area and height, entrance size, and light level. Around 30 percent of the colony participate in the nest siting, and these ant scouts go through different phases of commitment. Each ant first searches for nest sites, and when finding a spot, the length of the evaluation will be inversely proportional to the quality of the site. Once the site has been accepted, the ant moves into a canvassing phase, whereby she leads tandem runs, in which a single scout ant follower is led from the old nest to the new site. However, the newly recruited ants make their own independent evaluation of the nest and then return to recruit new ants. Since ants use more time to accept lower quality nests, the better quality nests will have a more rapid recruitment. Here, the ant decision-makers face a trade-off between speed and accuracy. Greater speed in making a final decision increases the risk of not choosing the best available nest site option. Recruitment via tandem runs is rather inefficient because ants only move at one third of their usual walking speed. When the size of support for one site exceeds a certain quorum threshold, a recruiting ant will move into a committed phase, and instead begin to carry passive adults and other items to the new nest site. These transports are done at a normal walking speed, marking a shift from slow to rapid movement into the new nest (Sumpter, Reference Sumpter2010).

Until recently, researchers have thought that dominant individuals lead decision-making in vertebrate groups (animals with backbones: mammals, birds, fish, reptiles, amphibians). However, recent studies show that consensus decisions are more common than previously thought, for example when animal groups decide in what direction they want to move. Only a small proportion of individuals in the group may possess the relevant information about the route. Some may also differ in their preferred direction. A consensus decision is then necessary to prevent the group from splitting. Typically, a group begins to move in a particular direction when a certain threshold of individuals make the same signal with their head movements (whooper swans), gaze in a particular direction (African buffalo), or use calling (gorillas) (Dyer et al., Reference Dyer, Ioannou, Morrell, Croft, Couzin, Waters and Krause2008).

Another example is meerkat groups which stay together during daily foraging (Figure 4.2). Some of their specific moving calls build on quorum decisions, which is used as an efficient temporal coordination tool of group movement. A quorum of at least two and usually three meerkats are necessary to enable the whole group to move to a new foraging patch. The quorum shows that an accumulation of evidence is needed, increasing the likelihood of the foraging patch actually being food-depleted. This decision-making system avoids that one individual makes the wrong conclusion. Neither dominance status, sex, nor age affects the calls and suggests they are made as independent individual assessment of the food patch quality. If none or only one extra individual join in on the moving call, the group will continue to forage in the same area. However, the moving calls are not used as a directional coordination tool. Because meerkats’ prey are widely distributed underground, it is more important for them to know when it is best for them to leave instead of where to go next. The system provides a simple mechanism to coordinate group cohesion while at the same time maximizing foraging success for the majority of the group (Bousquet et al., Reference Bousquet, Sumpter and Manser2011).¹

Figure 4.2

Meerkat (suricata suricatta) digging in the Kalahari Desert.

photo © Tim Jackson/Getty Images

It is also interesting that the quorum number is an absolute value, either two to three individuals. Other studies show similar results: it takes more than two fish to make a decision in groups of up to ten individuals. It appears that two to three individuals acting as signalers is a common requirement in several species, at least for group sizes ranging from six to 22 individuals. It shows that a quorum number does not need to be large to be effective since errors decrease exponentially with quorum size. If the probability that one meerkat wrongly concludes that it is time to leave a foraging patch is 5 percent, then the probability that two and three individuals will independently reach the same conclusion is 0.25 percent or 0.0125 percent, respectively (Bousquet et al., Reference Bousquet, Sumpter and Manser2011).

However, recent studies suggest that the response threshold in several different animal groups does not depend on the absolute number of other individuals exhibiting a certain behavior, but rather on a fraction of the perceived individuals who exhibits a certain behavior (Couzin, Reference Couzin2018). For example, a study of whirligig beetles, tested at what threshold the beetles initiated a flash expansion when observing a predator. The ratio of sighted beetles was manipulated so one could test whether the threshold was an absolute number or a proportion of the group size. The results supported the proportional hypothesis since the response occurred when more than 10 percent of the beetles saw the predator (Romey & Kemak, Reference Rosenberg, Lungren, Halabi, Willcox, Baltaxe and Lyons2018).²

Sumpter (Reference Sumpter2010) emphasizes that quorum responses can substantially reduce errors compared with independent decision-making. Positive feedback combined with quorum responses can aid accuracy in collective decision-making without requiring full consultation of all group members. While the quorum mechanism leads to improvement in accuracy over individual decisions, it does not achieve the same accuracy level as in majority decisions. For example, if 40 individuals each have a 2/3 probability of making the correct choice, the probability of a majority error is just 3.33 percent. In a similar group, a quorum response that is elicited when 5–15 persons make the same choice will produce an approximate error rate of 10 percent. In a quorum response, there is a risk that small initial errors can be amplified and lead nearly all individuals to make the same incorrect choice, which they would not have made by themselves. However, compared with making individual decisions the simple copying rule based on threshold responses substantially reduces the number of errors. The mathematical model suggests that response thresholds not only provide cohesion, but also facilitate accuracy. This is because quorum responses allow effective averaging of information without the need for complex comparison between the options. Evidence shows that in most cases, quorum responses allow for greater accuracy than complete independent behavior or just having weak responses to the behavior of others (Sumpter, Reference Sumpter2010).

4.2.2 Human Quorum Response as Swarm Problem Solving

The noun “quorum” is plural of qui in Latin, meaning “of whom.” The first quorum refers to commission papers that authorizes a group to be the justices of the peace. Today, the meaning of the term typically refers to the minimum number of members who must be present at a meeting in order to make official decisions. A human quorum often refers to the majority or supermajority of quorum (in most cases, the bylaws will state the rules for a quorum), but as in animal groups, a quorum can require a group minority significantly lower than 50 percent. It varies whether a specific percentage (quorum quotient) or a fixed absolute number is required to make decisions.

The main purpose of a quorum is to avoid a few members becoming too powerful when important decisions are made. Many democratic institutions also use quorum rules to ensure the “legitimacy” of decisions if it is likely that not all eligible voters will participate. For example, it may not only be enough with a majority, but the total number of votes will also need to exceed a particular threshold. Quorum rules are common in referendums like for example in Switzerland, which let citizens challenge a law approved by the parliament or propose a modification of the federal constitution. They organize several different types of referendums, including mandatory referendums that propose a modification of the national constitution, optional referendums which require that citizens collected 50,000 signatures against a law accepted by the national Assembly and demand a referendum, and there are also federal popular initiatives with voting on a change of the constitution, which require a minimum of 100,000 (“How to launch a federal popular initiative,” 2020). With 1,000 signatures in Kraków, Poland, a proposal can be presented to organize a citizens’ assembly, and with 5,000 signatures, the mayor is required to organize an assembly (Gerwin, Reference Gerwin2018). Town meetings is another example of a quorum response where those who show up make the decision. However, there are major challenges in this method since studies show that very few eligible voters show up and very few speak up in these settings. In Switzerland, direct democracy continues to play an important role at a local (cantonal) level, but it is increasingly as a referenda and not as the large gatherings where everyone meet together face-to-face. The Landsgemeinde or cantonal assembly only persists in two cantons (Fishkin, Reference Fishkin2018: 26, 47) (see Figure 4.3).

Figure 4.3

People raise their hands to vote during the annual Landsgemeinde meeting at a square in the town of Appenzell, April 29, 2012. Appenzell is one of Switzerland’s two remaining Landsgemeinden, a 700-year tradition of an open-air assembly in which citizens can take key political decisions directly by raising their hands.

photo Christian Hartmann/Reuters/NTB ©

With the emergence of new digital technology and an online setting, quorum response mechanisms are now also used in new ways. In certain types of synchronous decisions-making systems, individual votes can be graded and collective decisions are made when a certain threshold level of support is reached (Patel et al., Reference Pargeter, Khreisheh and Stout2019; Willcox et al., 2020) (see example in Section 4.4.2 Large Gatherings as Human Swarm Problem Solving). One interesting example is Kickstarter, which is a crowdfunding platform that gathers money from the public as a new way of financing new ventures and bringing creative projects to life. Here, the quantitative response threshold is not votes, but money. Project creators in need of economic support will describe the project on the website and choose a deadline and a minimum funding goal. The model builds on microfinancing and make it possible for anyone to contribute from anywhere in the world within a short fixed period (Kuppuswamy & Bayus, Reference Kuppuswamy and Bayus2017).

In 2017, Kickstarter reportedly received more than $1.5 billion in pledges from 7.8 million persons to fund approximately 200,000 projects. The projects range from the invention of equipment, art projects, design, technology, film, music, games, comics, and food-related projects. People who support Kickstarter projects are usually offered tangible rewards and the opportunity to buy some of the products for a reduced price (Kuppuswamy & Bayus, Reference Kuppuswamy and Bayus2017). The collective decision of whether to fund the project or not is left open to unknown others or outsiders in a global online setting. In some projects that aim to sell a product, it may be relevant to check whether the product is interesting for potential customers in the future. These online platforms enable people to create products that it would have been very difficult to fund in other ways. In this way, crowdfunding resembles arts patronage, where artists go to the audiences to fund their work. The difference is that the outreach is to potential backers from all over the world.

This fundraising resembles a quorum response because it builds on an “all-or-nothing” model. If the project is not fully funded within the deadline, the project owner gets no money at all. If the funding goal is overambitious, there is a risk that one may raise no funds at all. However, the project can continue to receive contributions until its deadline even after the funding goal has been reached. The crowdfunding process is also transparent in providing information about the total amount of money received at any point of time. Anyone can see how much money is needed to reach the pledge or the decision threshold point. There is also information on the number of backers and days of the crowdfunding period (Kuppuswamy & Bayus, Reference Kuppuswamy and Bayus2017). Micro funders have an updated overview of the aggregated collective contribution at any time. Because contributions are given as money, the size of the contribution is also much more flexible compared with votes.

4.2.3 Majority Decisions

Majority decisions is another decision threshold method that is particularly important in human decision-making and democratic political systems. When problems involve discrete alternatives, large groups will often use majority or plurality rule to make a decision. The most important theorem that explains the epistemic advantages is the Condorcet Jury Theorem from 1785. According to the theorem, majorities are virtually certain to be right when some assumptions are fulfilled. The theorem states that if voters (1) face two options, (2) vote independently of one another, (3) vote honestly and not strategically, and (4) have, on average, a greater than 50 percent probability of being right, then, as the number of voters approaches infinity, the probability that the majority vote will yield the right answer approaches certainty (Anderson, 2006). These principles were first applied in the design of a jury system that aimed to determine the optimal number of jurors. Today, it is used in a much broader sense to prove how majority rule decisions can be better than individual decisions. It explains the relative probability of a given group of individuals arriving at a correct decision. The theorem also covers plurality voting with multiple-choice options (Anderson, 2006; Landemore, Reference Landemore2013: 70–72, 75).

4.3.1 Averaging as Swarm Problem Solving in Animals

The section on decision threshold methods describes situations where one individual has a piece of information, like the location of food, which is then transferred to others through positive feedback. It can then be effective to copy the behavior of the individual that possesses the relevant information. However, animals also make decisions when there are two or more options when none in the group knows more than the others. For example, when a group looks for food in an unfamiliar environment, each individual has some probability of making the “correct” decision, but no individual is more likely to be correct than any other (Sumpter, Reference Sumpter2010). Under such circumstances, animal groups will sometimes use an averaging strategy.

As already mentioned, the “many wrongs principle” refers to the general idea that social interactions reduce individual errors, improves navigational accuracy when groups move together. For instance, individuals which move together in herds, flocks or swarms, will continually adjust their route based on real-time perceptions of the movements of other agents. Simulations have demonstrated that averaging can describe local social interactions if individuals balance their own preference with how their neighbors move. These simple mathematical models assume that all individuals in the group are identical, follow the same interaction rules and have the same level of navigational information or error (Berdahl et al., Reference Berdahl, Kao, Flack, Westley, Codling, Couzin and Biro2018).

At first, one might think that averaging is a distinctly human decision method since it follows a relative complex statistical rule, but surprisingly, animal groups are also able to use this mechanism when navigating. Already in the 1960s, some researchers proposed that birds and fish moved in the average preferred direction of all individuals (Berdahl et al., Reference Berdahl, Kao, Flack, Westley, Codling, Couzin and Biro2018). Recent empirical studies have also proven the existence of such a mechanism. One example are wild baboons, which prefer a process of shared decision-making instead of following dominant individuals when they navigate (Figure 4.4). If the disagreement on the angle of the direction of movement is above 90 degrees, the baboons will choose to travel in one of two preferred directions. In this case, majority rule counts, and every one will eventually move in the same direction. However, below a critical angle, if the differences in preferences are lower than approximately 90 degrees, the baboons’ compromise. The group will then move towards the average of the preferred directions (Strandburg-Peshkin, Farine, Couzin, & Crofoot, Reference Strandburg-Peshkin, Farine, Couzin and Crofoot2015). Honeybee swarms use the same mechanism. Prior to lift-off to a new nest site, the bee dances encode the direction to the chosen nest site with some individual differences. The actual flight direction will then be close to the average direction advertised by the different bees in their dances (Oldroyd, Gloag, Even, Wattanachaiyingcharoen, & Beekman, Reference Oldroyd, Gloag, Even, Wattanachaiyingcharoen and Beekman2008).

Figure 4.4

Olive Baboons crossing Uaso Nyiro River in Kenya.

photo Don Farrall/Getty Images ©

When averaging, both baboons and honeybees improve their navigational accuracy because of the “many wrongs principle” (Simons, Reference Simons2004). When all individuals want to reach the same target destination, they will navigate according to their unique directional information such as visual landmarks, internal compass, and smell and so on. Each individual will therefore navigate with some error, but when this error is unbiased, the average direction of the group is more likely to be correct than a random individual in the group. Assuming there is no cost to aggregating information, navigational error in the average direction decreases in proportion to the group size. This is analogous to the central limit theorem that shows how the standard error shrinks when the sample size increases. Averaging effects reduce “noise” at the individual level of information, and produce more accurate collective actions (Berdahl et al., Reference Berdahl, Kao, Flack, Westley, Codling, Couzin and Biro2018; Krause et al., Reference Krause, Ruxton and Krause2010; Strandburg-Peshkin et al., Reference Strandburg-Peshkin, Farine, Couzin and Crofoot2015; Sumpter, Reference Sumpter2010).

However, animal groups do not explicitly average individual estimates in a group because they can only observe their near-neighbors. Instead, the collective behavior relies on individuals having access to different information. According to “the many eyes principle,” animal groups can integrate more information about the environment because it is distributed among all the individuals. Therefore, the dominant male in the baboon group does not have a higher chance of getting followers, in decision-making on group movements. These daily decisions are shared equally between the members of the group (Strandburg-Peshkin et al., Reference Strandburg-Peshkin, Farine, Couzin and Crofoot2015).

4.3.2 Human Averaging as Swarm Problem Solving

By now, there exists a lot of research that demonstrates averaging effects within the “Wisdom of Crowds” literature (Surowiecki, Reference Surowiecki2005). A classical example is the jelly-beans-in-the-jar experiment, in which the group’s estimate is superior to the vast majority of the individual guesses. In one study with 850 beans in a jar, only one of the fifty-six individuals beat the crowd guess of 871. If ten different jelly-bean-counting experiments are done successively, it is likely that one or two students will beat the group each time. However, it is very unlikely that the same student outperforms the group. Over ten experiments, the group’s performance or the crowd will almost always be the winner compared with single individuals (Surowiecki, Reference Surowiecki2005; Treynor, Reference Treynor1987).

The basic requirement in human averaging is that estimations, predictions, or judgements can be quantified. The crowd will often be studied as the aggregation of separate individual judgements. Typically, the crowd will solve simple tasks that assume the existence of a correct solution, such as predicting changes in the stock market or betting on a sports event. Each member of a crowd will submit some relevant information (signal) and some random errors (noise). When these errors are truly random and not systematically biased, the average will perform very well because the errors cancel. A good example of the crowd estimate is the temperature in a room, since individuals use uniquely different strategies when they guess the temperature (Davis-Stober et al., Reference Davis-Stober, Budescu, Dana and Broomell2014; Surowiecki, Reference Surowiecki2005).

If certain conditions are fulfilled, a group can be remarkably smart when their averaged judgements are compared with the judgements of individuals. The individual heterogeneity in the group makes the aggregate more accurate (Lorenz, Rauhut, Schweitzer, & Helbing, Reference Lorenz, Rauhut, Schweitzer and Helbing2011). From one perspective, this effect is primarily a statistical phenomenon that requires some type of averaging technique. A typical definition of “crowd wisdom” refers to the performance of a group average compared with an individual selected randomly. If guesses exhibit a random deviation from the correct answer, these deviations tend to cancel each other out when a large number of them are aggregated. When inaccurate perceptions are diverse, the shortcomings of the ones tend to compensate for the shortcomings of the others. This gives a more accurate, global estimate. Other definitions of crowd wisdom are more mathematically orientated, comparing the mean of the individuals with the mean individual or defining accuracy as the average squared error of prediction (Davis-Stober et al., Reference Davis-Stober, Budescu, Dana and Broomell2014).

Several of the citizen science projects from Chapter 2 use averaging techniques to aggregate independent volunteer contributions. The same micro task is done by several persons independent of each other a certain number of times. This increases the likelihood of getting correct and valid information. For example, in the Galaxy Zoo project, hundreds of thousands of online volunteers helped astronomers by classifying the shapes of astronomical objects. Even though some single volunteer made mistakes, this became less of a problem when many volunteers looked at that same object. The group results were very accurate and showed that the crowd can perform well on relatively simple tasks.

A comparison of several wisdom of crowd studies found that simple crowd average is robust across different aggregation and sampling rules. In most cases, the simple average of individual judges is wiser than a single individual estimate. If the true score is well bracketed by multiple estimations (near the median or average), the aggregate accuracy will perform much better than the typical judge in the group. This crowd wisdom effect is present even when judges are individually biased and the crowd aggregate is not particularly accurate. Unless it is easy to identify the best individual across tasks that are done repeated times, the best option is instead to choose the unweighted aggregate of the crowd if the size is large. Over time, even the best performers will lose against the crowd average (Davis-Stober et al., Reference Davis-Stober, Budescu, Dana and Broomell2014). Although the simple average or mean is the most popular aggregation technique, others have argued that median is a viable option. When group size is small, medians are less sensitive to extreme member estimates and may provide a more accurate result (Tindale & Winget, Reference Tindale and Winget2019).

There are also other treats against averaging. If individual have very little background knowledge, the crowd aggregate may be very bad. In one study, the crowd made a very poor estimate when asked how many times a coin must be tossed for the probability that the coin shows heads (and not tails) on all occasions to be roughly as small as that of winning the German lotto. Here, the estimate of a single “expert” is better, as a person with competence in mathematics can quickly estimate the correct answer to be 24 coin tosses. Compared with the jelly bean experiment of the temperature task, the coin example shows not only that those individuals are imprecise, but there is also a huge systematic bias. Most real-life problems include both imprecision and bias, and it is not always easy to distinguish these from each other (Krause et al., Reference Krause, Ruxton and Krause2010).

One way of improving the averaging methods is to weight individuals differently, for example by giving more weight to expert members (Tindale & Winget, Reference Tindale and Winget2019). However, there is still a risk that the decrease in variance of predictions can offset bias in future aggregations. Another key concern is the role of social influence. It is almost impossible to collect independent opinions in society because people are part of social groups and will be influenced by each other (Davis-Stober et al., Reference Davis-Stober, Budescu, Dana and Broomell2014). An important condition in the original “wisdom of crowds” approach is that the estimations need to be made independent of each other (Surowiecki, Reference Surowiecki2005). While animal groups are very effective in producing individually independent information, humans are much more vulnerable to social influence. There is a risk that negative social influence can reduce the diversity of perspectives. For example, one study found that when the crowd received information about the group estimate, the individuals changed their estimates and performed worse as a group.

In the first round of the study, all subjects answered independently. Afterwards, the subjects were allowed to reconsider their response after having received full information of the group response. The new estimates narrowed the diversity of opinions in a negative way even when the individuals were not allowed to discuss the task with each other. One explanation is that when individuals become aware of the crowd estimate, they may move closer to the average because they assume that the crowd is wiser. If all predictions are more narrowly distributed around a value, this “range reduction effect” makes the crowd less reliable. The negative effects of social influence will also be smaller if the individuals are more confident in their own estimates (Lorenz et al., Reference Lorenz, Rauhut, Schweitzer and Helbing2011). The Delphi method builds on this assumption (see Section 4.5.2).

4.4.1 Large Gatherings as Swarm Problem Solving among Animals

Are humans the only ones who let a large number of people come together to solve a problem? Not entirely. Arguably the most famous example in the animal world is the “waggle dance meeting” which is an event honeybees arrange to find out where to move their nest. The house hunting will usually begin when colonies become overcrowded in their nesting activities. About a third of the worker bees stay at home and rear a new queen, while the rest, a group of ten thousand bees, leave together with the old queen to create a daughter colony. The migrants travel about 30 meters before they stop and form a beardlike cluster, where they stay for a few days. From this place, several hundred house hunter bees will travel out and explore 70 square kilometers (30 square miles) of the surrounding landscape for potential home sites. They will usually identify around a dozen potential home sites, which are evaluated by several bees to check if they are sufficiently spacious or provide good protection. What is remarkable is that the bees almost always select the single best site from the options they have first identified (Seeley, 2010: 6). In this process, they utilize a range of strategies that are also relevant for human swarm problem solving.

The scout bees follow three steps in their collective decision-making process. First, they search widely for prospective nesting sites and identify all the available options in the surrounding area. They look for small, dark openings that can provide a roomy and protective nest cavity. None of the bees checks the same area; they are able to maximize the diversity of their searching behavior, and thus optimize the chances of finding an excellent home. The differences in flight routes may be due to where they have previously worked as foragers or differences in their “personalities.” Since the search group is so large, with several hundreds of bees participating, they are usually able to identify the best sites very quickly, usually within hours or a few days (Seeley, 2010: 224, 234–235).

The second step is that the bees meet at the cluster and freely share information about all the available options. The scout bee that has located a good potential nesting site announce the discovery through a waggle dance which aims to recruit other scouts to the fly-out and evaluate the sites. These recruited bees will then fly out, assess the site independently, and then return to dance for that site. Dances are more frequent for better sites, leading to a faster recruitment of scouts. This is how the positive feedback loop of recruitment to the different sites begins (Sumpter, Reference Sumpter2010).

What is extraordinary with the honeybee waggle dance is that it gives specific information about the distance, direction, and desirability of the site (Figure 4.5). The duration of each waggle run is the distance coding and gives information about the length of the outbound flight. Second, the waggle run is positioned as a direction coding by running at the same angle as the proposed outbound flight relative to the sun’s direction. The dance is a specific flight instruction: “Should we consider this site which is located X degrees to the right (or left) of the sun and Y meters away.” In addition, the number of dance circuits inform the relative desirability of the site. The better the site is, the stronger the advertising dances will be, resulting in a stronger positive feedback for this site. The dance attracts the other uncommitted scout bees to a specific site, and the scouts who made the original discovery tend to be especially persistent in sharing their information (Seeley, 2010: 11, 224, 226–227 235–236).³

Figure 4.5

The honeybee waggle dance. The direction the bee moves informs others about where the site is. The duration of each dance informs about the distance to the site.

photo Paul Starosta/Getty Images ©

One can look at the waggle dances as a large gathering with competing “dance” advertisements for different candidate nest sites. At any given point of time, some scout bees will be committed to a candidate, while others are still uncommitted. A committed scout will advertise “her” site to uncommitted scouts and recruit them to visit the advertised site. When the recruited bees return, they advertise the same site and begin to recruit even more scouts to the particular site. Supporters of one site can also become apathetic and rejoin the neutral voters. Since the bees that have found the best site will dance most intensively, they will gain supporters more rapidly and these supporters will move back to a neutral status more slowly. The interest in some sites will shoot up, while others fade away.

All bees are free to advocate any site, and all views are voiced and respected. What is important is that the scout bees do a personal, independent evaluation of the different sites. Each individual decides whether she want to fly out to the site and whether she want to advertise it when returning. No scout bee will follow another dancer without inspecting the site. This is important because if scout bees blindly copy other bees, they would make biased decisions by overemphasizing the reports from the first scouts. The aggregated information builds on an open debate with contributions from dozens, if not hundreds of scout bees with independent opinions (Seeley, 2010: 224, 226–228, 235–236).

The positive feedback mechanisms aim to recruit a sufficient number of scouts to one site to pick a winner. Even when the best site is discovered several hours after the other candidates, it will still quickly dominate the competition. The decision-making process is essentially a competition between alternatives to accumulate support, and the winning alternative is the one that first surpasses a critical threshold of support from the bees. When the scouts visiting one of the potential home sites exceed a specific threshold number, a quorum response is initiated which suddenly makes them return to the swarm. There is enough evidence to make the best decision. Back in the swarm, the scout bees who are convinced begin using piping signals to inform thousands of nonscout bees to begin warming their flight muscles. These preparations even start before all scouts have reached consensus since it is vital to speed up the process. Quorum responses ensure that the consensus decisions are both very accurate and time efficient since not all have to agree before a decision is made (Seeley, 2010: 8, 230). At the same time, the honeybees show that their solutions are surprisingly accurate (Seeley, 2010: 8, 226–230).

The bees’ survival depends on the decision about their new home. This is why they expend a lot of effort in searching for possible home sites and debate it for several days. The large gatherings of honeybees are interesting also in relation to human swarm problem solving, both in how all relevant options are identified, how this information is effectively shared, and how accurate decisions can be made more quickly through a quorum response (Seeley, 2010: 226–230). If we look at the basic idea of deliberative democracy, there are several similarities. People should listen to each other, include all relevant arguments, and criticize them in a fair way. Without these qualities, democracy can easily end in manipulation and misled opinions (Fishkin, Reference Fishkin2018).

4.4.2 Large Gatherings as Human Swarm Problem Solving

Hackathons

Obviously, there is a huge variation in how humans use large gatherings to solve problems together, also in nonpolitical areas. In the offline setting, the hackathon is one such example of a gathering with up to 1,000 participants (Figure 4.6). It is an event where people who not usually meet, gather for a few days to solve a problem together. Most hackathons center on software development. For instance, Google, Facebook, and open-source software projects like Linux host hackathons to rapidly advance work on specific development issues. In addition, universities and national and local government agencies increasingly arrange hackathons to build technology that addresses different societal issues, such as helping the elderly cope with dementia. Some events may have as many as 1,000 participants (Trainer, Kalyanasundaram, Chaihirunkarn, & Herbsleb, Reference Trainer, Kalyanasundaram, Chaihirunkarn and Herbsleb2016). A hackathon is also called a “hackfest,” which is an abbreviation of hacking festival. Codesprints or codefest is another term that avoids some of the negative connotations associated with the term “hack.” These sprints are usually organized as an intensive computer-programming event with specific goals and a short timeframe. However, most hackathons are quite open-ended and exploratory, with various activities going on at the same time. At the end of hackathons, individuals or groups will usually present or demonstrate their results (Briscoe & Mulligan, Reference Briscoe and Mulligan2014).

Figure 4.6

Hackathon in Berkeley, California in 2018. Students work at Cal Hacks 5.0, the largest collegiate hackathon, in Berkeley, CA, November 3, 2018.

photo Max Whittaker/The New York Times/NTB

Like with the honeybees, the participations will work hard within the short time period of the event. Typically, a hackathon will last between a day and a week in length. Eating and sleeping is often informal, and sometimes people will even sleep on the site. Participants will usually need computer programming skills; the exception is some hackathons organized for educational or social purposes. Participants must also be able to work comfortably with new people in small informal teams. This includes intense work conditions with time pressure. At the end of the hackathon, they must be able to present the work to others in a compelling way in a short time (i.e., pitching to potential investors) (Briscoe & Mulligan, Reference Briscoe and Mulligan2014).

Hackathons will usually begin with a plenary presentation about the event and the contest format, including the challenge prizes if available. Sometimes, the prizes will be a substantial amount of money. A panel of judges will then select the winning teams, and prizes are given. The judges can be organizers, sponsors, or peers. It varies to what degree information is shared online before the conference starts. The number of participants and the organization of teams will depend on the concrete tasks. Usually, the participants suggest ideas and form teams, based on individual interests and skills. Sometimes they will pitch their ideas to recruit more team members (Briscoe & Mulligan, Reference Briscoe and Mulligan2014). This is somewhat similar to how the honeybees also attempt to recruit other scouts to join them in investigating one specific site.

Although the hackathons are brief, one of the expected benefits is to build a community (e.g., often only a few days). When the participants observe and interact with another, they share the feeling of being at the same place. This proximity can contribute to the development of durable social ties. During the hackathon, it is important that the interpersonal relationship is of such a quality that people feel free to ask and offer help, and work openly so others can observe their work. By getting in contact with others, participants have the opportunity to identify common interests. If they share the same interests, it is more likely that they will trust each other and want to work together (Trainer et al., Reference Trainer, Kalyanasundaram, Chaihirunkarn and Herbsleb2016). Like with the honeybees, the hackathon let all participants move freely around, interacting with whomever they want.

Because of time pressure in completing work within the deadline, participants learn a lot about each other. However, one study still found that some participants were not comfortable asking for help, showing the importance of participants becoming acquainted. Some participants also maintain contact after the hackathon (Trainer et al., Reference Trainer, Kalyanasundaram, Chaihirunkarn and Herbsleb2016). Hackathons illustrate that an offline setting can be used to let a large gathering of people solve problems in effective ways within a short period.

4.5.1 Heterogeneous Social Interaction in Animal Swarm Problem Solving

Do individuals in animal groups usually behave the same way when they solve problems together? While averaging methods and decision threshold methods assume that individuals are identical units, there is today increased interest in how individual differences influence group behavior. For instance, genetically diverse honeybee colonies maintain a more stable nest temperature than genetically uniform bees. The reason is that diverse bees respond at different temperatures, thereby avoiding “all or nothing responses” that could easily overshoot the target temperature (Sumpter, Reference Sumpter2010).

In other animal groups, group heterogeneity includes the presence of both leadership and other specific social structures (Jolles, King, & Killen, Reference Jolles, King and Killen2020). Individuals will fulfill different roles in a group when they solve a problem together. When chimpanzees hunt monkeys in groups, they take complementary roles. The driver chases the prey in a certain direction, while the blockers prevent the prey from changing directions. Although this type of group hunting looks like genuine collaboration, the most likely explanation is that they follow simple interactional rules. Each animal fills whatever spatial position is still available at any given time. Encircling is, in this way, accomplished in a stepwise fashion. The group hunting does not require a prior plan or agreement; each individual chases the prey from its own position (Moll & Tomasello, Reference Moll and Tomasello2007).

Complementary roles in a group hunt can be explained as simple associative learning. One simple rule is that each individual follows their preferred stalking pattern and goes straight towards or circle around the prey. The timing of actions between the animals needs to be synchronized to make the hunt effective. For example, when wolves fan out and encircle prey, they follow two simple rules; get to the closest safe distance from the prey, and get the best possible view of the prey (Figure 4.7). By following these two rules, each individual will at the same time move both towards the prey and away from other individuals, so those in front do not obstruct their view (Bailey, Myatt, & Wilson, Reference Bailey, Myatt and Wilson2013).

Figure 4.7

Cow moose defends her newly born calf from the Grant Creek wolf pack while surrounded in a tundra pond in Denali National Park, Alaska.

photo Patrick J. Endres/Getty Images ©

Body posture may be important as communication, particularly in instances where the prey is only visible to the first animal. It provides information about prey position and direction of travel to the other pack members. For example, when lions see prey, they adopt a ridged, alert posture which give the other lions information about the prey’s presence and location. In addition, individuals often choose to adopt a similar posture or speed of travel to that of conspecifics during hunts resulting in greater synchronization. This copying of behavior between individuals is effective because individuals base their decision both on information from the environment and from each other. In most circumstances, these strategies, which require a low level of cooperation with simple interactional rules, may be very effective (Bailey et al., Reference Bailey, Myatt and Wilson2013). Studies of schooling fish have also shown that they organize themselves in an attempt to obtain independent individual information. Their network of social influence is structured to reduce the probability that individuals obtain correlated (redundant) information from others (Couzin, Reference Couzin2018).

Furthermore, in most cooperative hunting species, there is some degree of information transfer amongst individuals in group hunting, achieved via visual, tactile, vocal or olfactory cues/signals or a combination of these. Depending on the hunting strategy, this can take the form of both inadvertent behavioral cues or intentional signals. For example, vocal communication is ineffective for predators that typically rely on ambush, because the sound would alert prey. Dogs, however, rely less on surprise and thus can use vocal communication. In high levels of vegetation with poor visibility, calls may help coordinate pack movements, but they do not communicate specific hunting behaviors (Bailey et al., Reference Bailey, Myatt and Wilson2013).

These studies are interesting because they illustrate that higher-level cognition is not necessary to perform highly organized cooperative hunts. Effective coordination is achieved by following simple interactional rules in combination with some degree of associative learning (Bailey et al., Reference Bailey, Myatt and Wilson2013). Although chimpanzees are “mutually responsive” and adjust their individual actions according to the actions of other individuals in the group, there is no indication of joint planning. Nor is there any indication of a chimpanzee leader which directs the group activity (Moll & Tomasello, Reference Moll and Tomasello2007). The collective behavior of these animal groups illustrates how simple interactions at the local level create complex patterns of coordinated activity at the system level.

Although these examples illustrate collective problem solving without leadership, many animal groups will still rely on a small minority acting as leaders. Leadership emerges when informed individuals successfully guide naive individuals towards favorable environments. Like elephants, smaller groups may recognize some individuals to be leaders, but this leadership is usually anonymous in large groups (Figure 4.8). For instance, if information sharing about who has the relevant knowledge cannot be directly signaled, leadership can instead be achieved when the informed subclass moves more quickly than the naive majority. When speed variations are used to transfer information, surprisingly few informed individuals are required to effectively lead a group (Berdahl et al., Reference Berdahl, Kao, Flack, Westley, Codling, Couzin and Biro2018). When moving together, individuals with faster speeds or slower turning behavior will tend to end up at positions towards the front and edge of groups. The leader in the front of groups will have a larger influence over group movements and decision-making because of how the information flows in the group. For example, fish leaders will elicit following from naive conspecifics by showing more directed movement paths or greater likelihood of initiating motion. In many cases, those individuals with relevant information or experience are more likely to get followers. For example, in groups of elephants and killer whales, knowledgeable and older individuals lead foraging decisions, especially when the environment is changing. Individuals which are central in social networks are also more likely to get followers (Jolles et al., Reference Jolles, King and Killen2020).

Figure 4.8

African Elephant herd walking on marshy area of Amboseli National park, Kenya. The oldest female is the leader of the herd.

photo Manoj Shah/Getty Images ©

However, there will be a conflict of interest between maintaining group cohesion and moving towards the individually preferred target. If the group becomes too large or too diverse, it may become fragmented. One mechanism that helps avoid this is that members of the group rotate at being leaders. Because it is costly to devote a lot of attention to gathering information, it may also be more effective to have some leaders who primarily focus on environmental cues and followers who predominantly rely on social cues. This group heterogeneity may be an outcome of evolution, rather than simply a consequence of age structure or mixing (Berdahl et al., Reference Berdahl, Kao, Flack, Westley, Codling, Couzin and Biro2018; Jolles et al., Reference Jolles, King and Killen2020).

Studies show that only a very small group of goal-oriented individuals is required to lead a large numbers of uninformed individuals to novel resources. Naive individuals can even improve collective navigation, because they, in line with the many wrongs principle, contribute with errors that can actually stabilize consensus decision-making and increase the speed and sensitivity of consensus (Berdahl et al., Reference Berdahl, Kao, Flack, Westley, Codling, Couzin and Biro2018; Jolles et al., Reference Jolles, King and Killen2020). Likewise, studies of human groups show that a small, informed minority (5 percent) could guide a group of naive individuals to a target without verbal communication or obvious signaling. When conflicting directional information was given to the informed individuals in the group, the time taken to reach the target did not increase significantly. It suggests that this mechanism can also be effective even when the informed subgroup disagrees on the preferred options. When there was a disagreement, the majority dictated the group direction (Dyer et al., Reference Dyer, Ioannou, Morrell, Croft, Couzin, Waters and Krause2008).

Another aspect of group heterogeneity is the possibility of social learning. Social learning allows knowledge possessed by informed individuals to spread through the group and across generations through unidirectional copying behavior. If naive individuals follow more knowledgeable group individuals along a path or a migration route when they travel, they may learn the route by being exposed to the cues associated with that route. This learning is unidirectional in the way that individuals gain personal information by following others who already have that information. Over time, they will become an informed subset. For example, cranes have no genetically encoded preferred direction in navigational tasks but will instead rely on social learning over generations. Because there are different levels of knowledge in the group, naive individuals can learn migratory routes that may be helpful in future journeys. In such groups, there will both be informed and naive individuals. Intergenerational leadership will be one way that social learning can emerge. For example, neither genetic, nor environmental factors, explain Atlantic herring annually returning to specific sites to feed and breed. The most likely explanation is that young individuals school with and learn from older and more experienced individuals. Light-bellied brent geese also choose staging and wintering sites in adulthood that are identical or very near to those of their parents, indicating social learning of migratory routes. In such cases, successful navigation will be more effective with leadership by the informed subgroup. The other alternative, navigating by the “many wrongs” principle and averaging estimates across the entire group, would be worse when a large group of naive individuals lack experience of the route (Berdahl et al., Reference Berdahl, Kao, Flack, Westley, Codling, Couzin and Biro2018).

4.5.2 Human Heterogeneous Social Interaction as Human Swarm Problem Solving

There is also CI research that examines heterogeneous interaction through collective problem solving in different social network structures. These social structures will follow specific interactional rules. For instance, an important part of the original wisdom of crowd approach is decentralization (Surowiecki, Reference Surowiecki2005). Centralized networks are organized around a core or a leader, while decentralized networks open up for more direct social interaction. Here, the emphasis is on utilizing local and specialized individualized knowledge and avoiding a too strong centralization of the collective work.

In contrast, centralized networks have a structure where communication flows disproportionately through one or more members instead of being equally distributed among all members. In highly centralized structures, the core, or a coordinator, will broker all interactions amongst the peripheral group members. This guarantees that the core has access to all critical information and sole responsibility for coordinating activities for the whole group. The potential disadvantage is that the periphery will then become completely dependent on what the cores decides to share of information. Individuals in the periphery cannot share knowledge or learn from each other directly, and this is assumed to inhibit the problem-solving process. The core may end up being a bottleneck if a large quantity of information must flow through it or it can lead the whole network astray with bad ideas (Shore, Bernstein, & Jang, Reference Shore, Bernstein and Jang2020).

4.6.1 Environmental Sensing in Animal Swarm Problem Solving

As mentioned in the previous section on large gatherings, the honeybees display a fascinating ability to maximize environmental information when they search for the best nest site in their surroundings. It is a matter of life or death for the bees, and they are usually able to identify all relevant options in the surrounding area. This is possible because the individual searching areas do not overlap with each other. Most other mobile animal groups will also aim to utilize individual sensing capabilities by collecting information about the surrounding environment in an effective way (Berdahl, Torney, Ioannou, Faria, & Couzin, Reference Berdahl, Torney, Ioannou, Faria and Couzin2013). Previous sections showed how groups are able to pool imperfect individual estimates according to the many wrongs principle and use this information to navigate noisy and complex environments.

Often, animals will combine environmental information and social information between members in the group. For example, birds will utilize the “many eyes principle” when they synchronize their decisions on when, and where, to move to find food or avoid threats. A bird spotting a danger will start to fly, and by this example set off the whole flock to fly away. Starlings synchronize their individual actions very rapidly (Figure 4.9). When a predator attacks, a few peripheral group members will make the first encounter. This elicits a sudden change in direction, which then spreads through the rest of the group. Because the birds have different spatial positions in the group, they acquire different information about the surroundings and utilize the “many eyes principle” when spotting danger (Couzin, Reference Couzin2018; Dyer et al., Reference Dyer, Ioannou, Morrell, Croft, Couzin, Waters and Krause2008).

Figure 4.9

Starlings move as one giant organism to synchronize their defence against predators, Kent, United Kingdom.

photo Sandra Standbridge/Getty Images ©

Likewise, giant honeybees synchronize their activity to avoid threats. Because they nest on a single, open comb, they are a target for predatory wasps. When attacked, the bees respond by create “shimmering” waves collectively. Initially, a subset of individuals starts a wave by rapidly raising and lowering their abdomens, making the other neighboring bees do the same. As with neurons and other “excitable” cells, individual bees will need to “recover” for a short period after one round of activity. This creates very visible waves of rapidly expanding rings or spirals across the colony surface (Couzin, Reference Couzin2018).

Individuals respond to the body orientation of near-neighbors by alignment. It requires that each agent both independently gathers information about the environment, but also imitates the behavior of others. These simple rules or behavioral algorithms provide the basis for the “many eyes effect” by letting individuals benefit from others, such as when detecting a predator or finding food. This collective navigation is possible even when individuals do not know which cue other group members respond to at any moment in time. It is enough to copy or imitate the response of others in the vicinity (Krause et al., Reference Krause, Ruxton and Krause2010). The group’s capacity for surveillance also increases with the number of alert animals. Fragmented individual information will be integrated at a group level and provide a better overall “picture” (Feinerman & Korman, Reference Feinerman and Korman2017).

Emergent sensing is a label used to describe how animal groups in different ways combine environmental information and social interactional rules, which can be different types of repulsion, alignment, and attraction (Berdahl et al., Reference Berdahl, Kao, Flack, Westley, Codling, Couzin and Biro2018; Puckett, Pokhrel, & Giannini, Reference Poblet, García-Cuesta and Casanovas2018). According to Berdahl et al. (Reference Berdahl, Kao, Flack, Westley, Codling, Couzin and Biro2018), emergent sensing occurs when a group is able to navigate even when no individual is aware of the correct direction. In a school of fish, each individual fish directs its behavior based on the perception of the position and speed of its immediate neighbors. For example, if an individual fish has no memory and is only able to make a scalar, one-dimensional, measurement of the environment, it will not be able to assess the gradient of an environmental cue. However, when information from multiple individuals is compared with each other, the group can collectively measure and follow a gradient in the environment. This is possible because a part of the group behavior is orientated towards the environment, like when a school of fish navigate through a changing “noisy” light field. Although these fish are not able to detect environmental gradients individually, the school still manages to swim toward darker waters because of a simple context-dependent rule: when observing the light field, golden shiners swim faster in bright regions and slower in dark regions (Berdahl et al., Reference Berdahl, Torney, Ioannou, Faria and Couzin2013; Puckett et al., Reference Poblet, García-Cuesta and Casanovas2018). The movement is not directed by the behavior of one or a few “leader fish,” but a self-organizing intelligent swarm system (Figure 4.10).

Figure 4.10

Bronze whaler shark swimming through a giant ball of sardines, waiting to feed on them. Off the East coast of South Africa.

photo wildestanimal/Getty Images ©

A study shows that when the fish make movement decisions, they respond more strongly to social influences like the location of near-neighbors compared with the environmental influence of light gradients. Fish located in bright regions will travel more quickly, but because the fish also attract each other, this creates a rotation in the school, turning the whole group toward the darker region. The swim speed differences within the group causes a turning toward those who move more slowly. The collective sensing of the group level is both a result of individuals adjusting their speed in response to local, scalar, measurements of light (environmental gradient) and the social attraction to others in the group. The group operates as a distributed sensor network (Berdahl et al., Reference Berdahl, Torney, Ioannou, Faria and Couzin2013, Reference Berdahl, Kao, Flack, Westley, Codling, Couzin and Biro2018). Another type of fish, the tetras, outperform many other types of fish because they can sense the environmental gradient individually. They rely more on environmental information and less on social information, and can therefore have more distance between the individuals in the group. Most groups will not only navigate on the basis of sharing of information within the group, but they will respond to local environmental cues like light, odor, temperature, or finding the winds or currents that provide a better migration route (Berdahl et al., Reference Berdahl, Kao, Flack, Westley, Codling, Couzin and Biro2018; Puckett et al., Reference Poblet, García-Cuesta and Casanovas2018).

Another interesting finding is that simulations of schooling fish show that the group-level responsiveness to the environment improves spontaneously as group size increases. Although increased group numbers reduce measurement error, the key determinant of improved performance is the spatial extent of the group in relation to the length scale of the environment. Groups that are able to span a larger area are more likely to capture variations in environmental cues that are necessary to elicit speed differences between individuals in the group. Each individual exhibits a rudimentary, nondirectional response to the environment. This emergent sensing creates a collective response to the environment not present at the individual level. The results suggest that the ability to respond to environmental information may decline if the group fragments or is reduced in size (Berdahl et al., Reference Berdahl, Torney, Ioannou, Faria and Couzin2013). Studies of salmon in the wild have shown that in years with more fish, navigation to natal streams is more accurate. The journey home may benefit from the many wrongs principle when crossing the ocean, consensus decision-making when choosing between two freshwater streams and emergent sensing when locating the odor of a river or entrance of a fish ladder (Berdahl et al., Reference Berdahl, Kao, Flack, Westley, Codling, Couzin and Biro2018).

Social learning within groups is also important. If the size and composition of the groups varies and animals move throughout the environment, there will be present a large local heterogeneity of knowledge about the environment. In such cases, animal groups can make the best decisions by harnessing information from every one and follow the most informed group members. Naive individuals can even contribute with random noise and errors that may lead to the discovery of improved routes over time. This interaction between multiple individuals can sometimes lead to the production of new knowledge. For example, a group can jointly discover an improved route, through “the many wrongs principle,” and individuals in the group will then learn this new route (Berdahl et al., Reference Berdahl, Kao, Flack, Westley, Codling, Couzin and Biro2018). By collecting both social and environmental information, a group of individuals can improve their collective decisions if they are able to balance this information in an efficient way (Puckett et al., Reference Poblet, García-Cuesta and Casanovas2018).

4.6.2 Human Environmental Sensing

In environmental sensing, the basic assumption is that large groups can perform better because they can access more environmental information. One way of maximizing relevant information from the environment is by having a broad outreach. Many CI projects build on open calls for participation where anyone can join and all who join have equal status. The communication is not targeted towards one specific person or group. Like with a warning cry, the call for participation is just “released” into the surrounding environment, which in this case is the Internet as a global environment. The aim is often to recruit the right problem solvers with relevant competence. The goal is to either find the unknown intelligent outsider or recruit a large enough group of people that can provide a collective estimation or solution to a problem.

4.7.1 Predefined Problems

If we look closer at all the examples in this chapter, we see that the problems are predefined in different ways. A project will describe an initial problem or challenge and formulate an “open call for help.” In the online setting, the outreach can be to a very large group of potential problem solvers. Some projects look for individuals with special expertise (e.g., IdeaConnection), but in several projects, such as within citizen science (e.g., Galaxy Zoo), anyone can participate. This also includes most of the crowdsourcing projects in Chapter 2. Because the outreach is broad, it is important to formulate the problem in a precise way, so it is easy for potential participants to assess whether the task is relevant to do. Some problems are well-defined because the tasks are relatively simple tasks and do not require much background skills (e.g., Galaxy Zoo project). Regarding complex problems in innovation contests, intermediaries will often support the solution-seeker in formulating the problem in an accessible way. Deliberative Polling and the Delphi method are other examples of complex problem solving that involve a high degree of uncertainty about the best options. Still, both these approaches are reliant on a precise formulation of the problem. In Deliberative Polling, participants receive briefing material that aim to give a balanced and comprehensive introduction to the problem in a short time. In the Delphi method, the problem is described in the questionnaire sent out to the participants. In both these processes, the solutions will also be presented as a statistical result. Disaster management is another example of a predefined problem that centers on an emergency. Although part of the challenge may be to get an overview of the situation and what actually is happening on the ground, there is still no doubt about the general problem whether it is an earthquake or wildland fire.

4.7.2 Prespecified Problem-Solving Procedures

In human swarm problem solving, there is usually no need to metacommunicate about the collective work because the problem, the interactional rules, and the aggregation rules are defined in advance. By minimizing the need for explicit coordination, problems can be solved more rapidly. Nor is direct coordination possible when the group size is large. Two examples are Deliberative Polling and the Delphi method, where both the interactional rules and aggregation rules have been formulated in advance in a quite detailed way. In a hackathon, there are fewer interactional rules and more participant autonomy. Still, the core of the collective work, like the sessions and the contest format, will have been planned.

As animal groups follow a few simple rules in swarm problem solving, so will human swarms do the same in this approach. However, the human swarm contributions are obviously much more heterogeneous, being anything from a vote, an argument, or an informational report. Problem-solving procedures, like interactional rules and aggregation rules, will also vary a lot. Still, both honeybees and humans will in this type of problem solving be similar in the sharing of a common interest and agreement on the objective (Seeley, 2010: 233–234).

4.7.3 Rapid Time-Limited Problem Solving

This chapter shows the importance of rapid problem solving. Animal groups operate according to a speed vs. accuracy tradeoff. Among ants, the evaluation time of different nests regulate decision-making because they use longer time to accept lower quality nests. To speed up decision-making, a relatively low quorum number is required. When a certain number of ants move in the same direction, all ants will suddenly switch from slow to rapid movements and begin moving to one of the new nests. Honeybees also act under time pressure when looking for a new nest site. A decision must be made within a few days. The bees elicit a quorum response long before a majority of bees has checked the site and the accuracy of the decision is still very high.

If we look at the examples of human swarm problem solving, they also highlight “decision speed.” Both hackathons and Deliberative Polling require a weekend. Decisions in the UNU platform happens within seconds, and in disaster management, even lives depend on rapid decision-making.

The challenge is to enable a large number of individuals to produce new levels of insight under significant time compression. Swarm problem solving is in a hurry or it has a tight schedule to follow. This includes both tasks that allow direct interaction and other projects where contributions must be made separately from each other. The rapid problem solving is typically made possible because everyone adheres to prespecified problem-solving procedures. Here, two types of rapidity are highlighted, solving a problem as fast as possible or within a prespecified deadline.

4.7.4 Individual Learning

It is not apparent that human swarm problem solving always promotes individual learning. In the original wisdom of crowd approach (2005), the ideal is to reduce negative social influence such as herding effects. The risk of individual learning is that it can reduce diversity of opinion and promote herding instead of informed opinions. According to “the many wrongs principle” incorrect guesses at an individual level can make the crowd wiser. This suggests a possible conflict between collective performance and individual learning.

This dilemma is present in several citizen science projects (e.g., the Galaxy Zoo project). When using averaging, it is usually important to gather independent contributions, which ensure the quality of the work. The single individual will then have no information about other contributions. Social interaction is avoided because it can introduce herding effects, groupthink, or systematic bias.

Another example is decision-making process in the UNU platform. It is performed so rapidly to reduce potential negative effects of long-term social influence. As biologists have noted, even naive individuals can improve collective navigation, just by contributing error. Although some individuals are not particularly accurate, they introduce valuable “noise” that makes the crowd wise relative to the individual. Another advantage with minimizing individual learning is that the task can be done faster and make the problem-solving process more time efficient.

Nevertheless, there is much more attention today around the possible positive effects of individual learning in human swarm problem solving. While wisdom of crowd literature originally highlighted the importance of making independent separate contributions, dependent contributions are today considered to be equally important. Even animal groups appear to be able to both share information and simultaneously make individually independent assessments.

In human swarm problem solving, individual learning within a group can also improve crowd performance if one avoids herding or conformity pressure (Shore et al., Reference Shore, Bernstein and Jang2020). However, there is a tension between the need for independent opinions and the need for some degree of information transfer. Learning and herding are two different types of social influence that can be present at the same time (Shore et al., Reference Shore, Bernstein and Jang2020). Collusion, alignment, and peer group pressure are constant threats when social interaction is possible. Groupthink (“Social proof”) is our tendency to assume that if lots of people believe something, there must be a good reason why. One important factor is to get people to pay much less attention to what everyone else is saying.

Still, there is a need for learning and deliberation between individuals. The challenge is to find the balance between independent thinkers who create their own opinions and do not simply follow the views of others and those who are able to build on other ideas. This can be described as an independence vs. learning tradeoff, which open for different participatory designs. Both Deliberative Polling and the Delphi method expect individual learning to happen during the collective problem-solving process. However, the processes differ because Deliberative Polling promotes direct interaction, while the Delphi method builds on indirect interaction. Participants only get access to aggregated group information. The emphasis is on knowledge sharing and ensuring informational diversity, but without the opportunity of having any discussions. This is very different in Deliberative Polling because participants are encouraged to discuss ideas, but still primarily in separate subgroups.

Individual learning can happen in several different ways in the human swarm, both through observational learning and conversational learning. In observational learning, individuals learn by observing what others are doing and what they are discussing. One relevant example is hackathons in an offline setting and the traces of discussions in an IdeaRally in the online setting (Chapter 2). Here, the transparency of the environment is key, as is how it supports knowledge sharing. In centralized networks, the core node will spread information to everyone in the crowd without creating the same conformity pressure (Shore et al., Reference Shore, Bernstein and Jang2020). The Delphi method is one example of how aggregated group results are shared with everyone. This is done anonymously through a facilitator. By not allowing direct interaction between participants, the degree of independent assessment is larger and the role of social influence is minimized. The goal is to maximize learning and minimize herding, like conformity pressure or uncritical copying of others’ behavior.

Another example of observational learning is how disaster management platforms give everyone an updated overview of what is happening on the ground. By effectively aggregate all information on one site, individuals will more quickly learn about the situation and act more appropriately. In areas where such incidents occur often, like frequent occurrences of wildfires or flooding, it is essential that individuals learn how to take such systems in use in effective ways.

Furthermore, conversational learning is another important part of many human swarms. Both a hackathon and Deliberative Polling center on conversational learning between participants. The discussions can last for two days, and because participants are together most of the time, this allows for intense discussions. There is also experimentation, with discussion in similar large groups in an online setting, such as the previously mentioned IdeaRally (see Chapter 2).

Deliberative Polling can be regarded as a decentralized network, which divides several hundred participants into separate discussion groups comprising 15 persons. Individuals will engage in conversational learning with “near-neighbors” in these subgroups, most of the time separated from others. This may reduce potential negative herding effects.

Compared with the Delphi method, the learning potential is likely to be larger in Deliberative Polling because it is easier for participants to elaborate on each other’s arguments. However, this also increases the risk of negative conformity effects. A facilitator is included to avoid such effects and ensure equal participation. Another aspect of this learning process is the briefing materials participants receive. They offer individual learning, but they may also unintentionally create negative herding effects. However, both Deliberative Polling and the Delphi method collect the final results anonymously to strengthen the independent voices in the process.

4.7.5 Summary of the Basic Characteristics in Human Swarm Problem Solving

In conclusion, the quality of human swarm problem solving depends on whether one is able to utilize sufficient diversity of perspectives. Most of the swarm designs aim to produce informational diversity by bringing in people with different backgrounds from different environments. As mentioned in the sections on averaging and decision thresholds methods, individuals may benefit from pooling information to overcome inaccurate estimates according to “the many wrongs principle.” These contributions will be aggregated and not recombined or synthesized. The sections on heterogeneous social interaction and large gatherings show how cognitive diversity can be utilized in accordance with the diversity prediction theorem (Hong & Page, Reference Hong and Page2004). Likewise, the section on human environmental sensing shows how environmental information can be maximized according to “the many eyes principle.” Large gatherings also stand out as one of the most interesting swarm mechanisms in an online setting (e.g., IdeaRally).

Honeybee nest siting is in many ways a prominent example that can provide inspiration for human swarm problem solving. When searching the surroundings for the ideal home, they utilize “the many eyes principle” by identifying all relevant options with an extraordinary precision. They then compare all contributions through the waggle dance and are almost always able to identify the best solution through a quorum response mechanism. They have perfected both the informational search process and knowledge sharing process afterwards so the whole process is completed within just a few days. (Seeley, 2010: 73–75, 224). We are still far from designing human swarm problem solving to be as successful as the honeybees, but by better understanding its basic mechanisms, one can hope that new technological inventions can make us better able to utilize this type of problem solving in both an offline and online setting.