The system of the swarm; what epistemic democrats can learn from
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The system of the swarm; what
epistemic democrats can learn from
wild honeybees
Johanna (Hanne) M. van Beuningen
11750294
1 February 2021
st
Supervisor: Paul Raekstad
Second reader: Enzo Rossi
Democratic Theory
A honeybee colony, both a society and a superorganism
Source: Honeybee Democracy (Seeley, 2010)
Thesis future planet studies
Major political science11750294 – Universiteit van Amsterdam - 2021
Abstract
This thesis presents the possibility for looking at Honeybee swarms in order to refine
the epistemic benefit of democracy. Epistemic democracy by Helene Landemore
argues for the importance of cognitive diversity to make smarter decisions. However
it has trouble dealing with pluralism and the cognitive limitation of humans within
the deliberation phase. The nest selection process of a honeybee swarm can function
as a model for human societies because of the scope and the complexity of the
swarm. By looking at the swarm and apply system theory, we can learn to approach a
collective rational decision. Hereby we can overcome human cognitive limitations
alike the wild honeybee.
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Introduction
Biomimicry can be defined as the process of looking at strategies found in nature
(bio) in order to mimic it in human design (mimicry) (Biomimicry Institute, 2020).
Using biology as a design tool originates from the industry of product design. As an
example, the front of the high-speed Shinkansen train in Japan was designed looking
at the beak of a kingfisher and sharkskin was used as inspiration for low resistance
swimming suits (Ibid.). In the same field, social biomimicry, looking at animal
behaviour to inspire human social relations (especially in relation to insects such as
bees and ants) has increased over the past years (Fewell, 2015). Insects as opposed to
other animals live together in large, complex communities, which make them
suitable for comparison to social and political theories (ibid.). This thesis will focus
on the honeybee colony and its way of collecting knowledge. Seeley (2010) observed
collective decision-making amongst hundreds of bee scouts that went out to look for
a potential new nest. Every scout bee flies out in search of such a new place and
reports back to the other scouts with an informational dance on the surface of the
swarm, the process continues for one or two days with a decision for the best new
nest spot at the end. The process where the honeybees gather information and make
a collective decision without an executive order has a similar structure to epistemic
democracy. Epistemic democrats argue for a collective intelligence, a functional
Legitimation of democracy on the account of the group being smarter than any
individual (Landemore, 2012a). This paper investigates how these decision-making
processes might enrich the field of epistemic democracy. This results in a research
question “What can epistemic democrats learn from honeybee societies? The
question will be researched and answered in the following manner. First, the
foundation of epistemic democracy will be discussed, with special attention to
Helene Landemore’s theory of epistemic democracy, which theory will be prominent
throughout this paper. Second, a short summary of the honeybee colony will describe
the honeybee swarm and its collective decision-making mechanisms, based mostly
on the research of behavioural biologist Thomas Seeley. As both Seeley and
Landemore use terminology such as democracy, common interest and intelligence in
different forms, it is important to avoid semantic ambiguity. The theory of honeybee
decision-making is therefore converted so that it is coherent with the vocabulary of
epistemic democrats. Following, the differences and similarities within each part of
the decision-making (aggregation of options, distribution of options and conclusion)
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are examined. Lastly, system theory is introduced and functions as a framework to
portray insights from the honeybee nest search.
First of all, according to Held (2019) democracy is defined as the rule of the people
and is built upon the presupposition of self-government of that people but has many
different forms. The fundament for this form of government comes from the
conception that a person is sovereign and therefore the sovereignty of a state lies
with its people. Such people can institutionalize the use of this sovereignty by
forming a government. In practice it means that the people have the right to voice
their opinion and influence state affairs often though some form of voting procedure
(Held, 2019).
Democracy originates from old Athens where the people were gathered roughly forty
times a year to vote on public matters (Held, 2019). These ‘people’ only included the
free men of Athenian descent, yet this was already significantly different from the
then common aristocracies and autocracies (ibid.). Aristotle furthermore argued that
democracy should consist of deliberation of all citizens. He did not argue this out of
fairness but for its instrumental benefits, as democratic decisions are better in line
with the interests of the people than non-democratic ones (Aristotle, 1998 129b12-
20). There has been much contestation of the different conceptions of citizenship,
authority and the relationship between the two, yet this will not be a central topic.
Rather, this paper focuses on the discussion of the democracy’s legitimacy.
Furthermore, democracy can be seen as intrinsically good but more often it is
understood as a means to achieve liberty, equality and good decision-making or
justice amongst others (Held, 2019; Landemore, 2012a). Amongst the arguments, for
the legitimacy of democracy there is a division between instrumental and non-
instrumental arguments. Non-instrumental arguments understand democracy as
intrinsically good since it is a way of treating people as equals (Singer, 1973). Others
emphasize it ensures liberty as democracy enables a person to have control over their
life (Gould, 1988).
An instrumental argument for democracy is that a government has an incentive to
listen to the lower classes in society as they have political power, even when no other
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power is available to them (Mill, 1861). Both Rousseau and Mill have argued for the
benefits of democracy on individual character development because it makes people
more autonomous compared to aristocracies and autocracies (Mill, 1862; Elster,
2002). The instrumental argument presented by Aristotle is the foundation for
epistemic democracy: He stated that the governing body ought to include everyone
because it would lead to better decisions (Aristotle, 1998 129b12-20). Landemore
(2012) sees the epistemic value as a justification for democracy. She argues that
under set conditions any group is able to make better decisions than even the
smartest individuals within it. This gives democracy an epistemic advantage over
autocrats and oligarchs (Landemore, 2014). The epistemic advantage of the many is
used by many political theorists but is most prominent amongst epistemic democrats
(Mansbridge et al., 2012). This argument could be considered an empty conception
of democracy because of the absence of normative claims and ideals. Yet it is also an
argument that reaches beyond the border of democratic government, since the
instrument of democratic decision-making becomes something that might appeal to
those not satisfied with the ideational arguments for democracy (Landemore, 2012a).
It becomes an intellectual argument for democracy independently of any moral
arguments for or against democracy. The focus on the quality of democracy to make
decisions that are better aligned with a common good is usually perceived as
epistemic democracy.
Epistemic Democracy
Joshua Cohen (1986) was the first to mention the definition of epistemic democracy;
he characterized epistemic democracy as a form of popular democracy focussed on
the will of the people. Cohen correctly recognizes several prejudices founded in
epistemic democracy. He states that what he calls epistemic populism assumes three
things within the voting procedure: (1) an independent standard of correctness, (2)
people that vote according to what they think will lead to a common good and (3)
decision making as an adjustment of beliefs. Additionally Cohen (1968) states that a
critical factor within this theory is the acknowledgement of a common good.
Within the spectrum of theories of epistemic democracy there is a distinction
between deliberative and non-deliberative epistemic democracies. Condorcet’s jury
theorem is the most important non-deliberative theory of epistemic democracies
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(Estlund et al, 1989). Condorcet mathematically argued for the epistemic benefit of
numbers over cognitive ability of the few. His argument lays on the preposition that
an individual has slightly more than fifty per cent chance to choose the option that
leads to the desired outcome. An increase in individuals will exponentially increase
the possibility of getting the correct outcome. Condorcet’s jury theorem advocates the
importance of numbers for good decision-making.
Deliberative theories of epistemic democracy are more widespread since it is closer
related to the more prominent school of deliberative democrats (Estlund, 2008;
Landemore, 2012a). In practice, deliberative democracy can strongly resemble
epistemic democracy, but the ideal and emphasis differ. Deliberative democrats
emphasise the intrinsic value of deliberation regardless of the outcome as opposed to
epistemic democrats, who see deliberation as a means (Estlund, 2008). However,
both adjoining schools of thought emphasize the importance of the deliberation
quality, within a democracy (Landemore, 2014; Mansbridge et al., 2012). In addition,
there is discussion about the number of participants and whether or not these
individuals have to differ significantly, and if so, what the differing aspect should be
(Bohman, 2006; Landemore, 2012; Mill, 1861)
As mentioned earlier, Aristotle argued that within deliberation every citizen had to be
included since it would lead to better decision-making. This, however, is unrealistic
when we consider modern day societies both because of scope and pluralism. There
seems to be a threshold from which the epistemic advantage is replaced by chaos.
Amongst epistemic theorists the number is the first issue of contestation besides the
critical qualities of the group. Mill (1861) is commonly seen as a deliberative
democrat but argues for an epistemic benefit, only, when the full range of opinions is
included. The deliberation therefore not necessarily has to include all citizens.
Bohman (2006) criticizes Mill in his sole focus on opinions. Instead Bohman (2006)
argues that diversity in opinions, perspectives and values ought to be included within
the group of deliberators. These conditions are in Bohman’s opinion essential to
provide more robustness to the process and especially to the outcome of the
deliberation. The technical Diversity Trumps Ability Theorem likewise argues that a
randomly chosen group of problem solvers can outperform a group of the brightest
problem solvers because of its diversity (Hong & Page, 2004). Landemore builds
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upon Hong and Pages (2004), Estlund (1989) and Mill’s (1861) research for her focus
on cognitive diversity as a means to achieve better decision-making. She places her
theoretical framework between the aggregative model and the deliberative model
(Landemore, 2012a). The aggregative model perceives democracy as an aggregation
of interests, yet Landemore is solely interested in the aggregation of judgements and
predictions. The deliberative model perceives deliberation as the centre of democracy
while Landemore perceives deliberation as one of the options in order to achieve
collective wisdom (Landemore, 2012a).
Specifically, Helene Landemore’s (2013) notion of epistemic democracy appeals to
the claim that the many are smarter than the individual because of the existence of
what she calls collective intelligence. Her concept of collective intelligence is the
essence of democratic reason necessary for an epistemic democracy. Individuals that
unite and achieve collective intelligence are smarter than the smartest individuals
within it and are thus able to make better decisions. Collective intelligence is the
foundation for democracies’ epistemic advantage and is a function of average ability
and cognitive diversity. She builds upon Hong and Page’s theorem (2004) that states
that the diversity of the group is more importance than the intelligence of the
individuals within such group. According to Landemore (2012a) cognitive diversity is
preferable as opposed to other forms of diversity promoted by other democratic
theorists. For a people to solve a political issue it is of utmost importance that various
people interpret the case in distinct ways. Every person interprets the world in a
different way and applies his or her internalized predictive model onto it. To clarify:
Predictive models are formed over the full course of someone’s lifetime and represent
an individuals’ model of what life entails. Likeminded individuals have similar
predictive models; the more two individuals differ the harder it becomes to
understand the other’s predictive model. Therefore, diversity is a quality of a group
and the same intelligence is impossible to achieve as an individual (Landemore,
2012b).
In addition, Landemore (2012a, 2012b, 2013) distinguishes herself from theorists
like Condorcet by expressing the importance of diversity within the decision-making
group as opposed to the number of individuals within the group. However, when
given the choice to rise the individual ability or the number of people taking a
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decision the latter is considered more critical. This results in a hierarchy of group
selection criteria; the most important is cognitive diversity because this is the
essence of the epistemic advantage of democracy. The second criterion is numbers
since it will naturally increase cognitive diversity. The least important of this ranking
turns out to be individual ability, and is therefore considered not essential for an
epistemic benefit (Landemore 2012a, 2012b, 2013).
Let me elaborate with an example of a decision-making group that has to increase
their collective intelligence. In order to achieve maximal cognitive diversity one
would have to do a full in depth popular inquiry, which is both expensive and time
consuming making it irrational. A random sample would be the next best thing:
Although a random sample would have sub-optimal ability, the diversity of the group
is likely raised. The least favourable is the selection of the brightest individuals
because this will only provide a limited range of predictive models: The brightest are
often schooled in similar forms and thus have similar predictive models (Landemore,
2013).
Further, Landemore (2012a) determines three phases in democratic decision-
making. She argues that in every stage cognitive diversity is more important than
individual ability. The first stage is (1) the aggregation phase where problems and
solutions are determined and aggregated. (2) The deliberation phase consists of the
exchange of predicative models and knowledge and finally (3) the decision phase is
where the actual decision is made. Deliberation can range from informative to
persuasive and Landemore’s formulation of deliberation can be placed between the
two as consideration. In order to let the decision-making be effective there is a
threshold after which more people that are included in the deliberation will lead to
chaos and does not provide any material insights anymore. In order to prevent chaos,
Landemore proposes representation as a possible solution (Landemore, 2013). Yet,
because of the superiority of cognitive diversity over ability she proposes selection by
lottery as opposed to an election as we know it in today’s society. A selection by lot
will naturally increase cognitive diversity. Yet there are barely any modern day
examples of binding deliberative bodies selected by lot, perhaps because it is difficult
to accept that diversity will lead to better decision-making and selection by lot can
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seem dangerous or irresponsible (Landemore, 2014). This complicates the argument
for Landemore.
The base of the epistemic argument for democracy could lie in Nietzsche's
perspectivism, despite Nietzsche’s own aversion towards democracy. Nietzsche
argues that knowledge is not based upon an absolute objectivity or metaphysical
reality but is fragmented and dispersed into perspectives (Anderson, 1998).
Anderson (1998) argues that Nietzsche’s perspectivism is placed between strong
realism and extensive relativism. Meaning that it rejects the existence of an
independent truth but perceives truth can be approached through overlapping
perspectives. Meaning, one can only approach objectivity or truth by comparing
one’s own perspective with the perspective of someone else (Hales & Welshon,
2000). This implies that every person’s thinking is biased. Everyone has to see the
knowledge he or she produces within the light of his or her own perspective. At the
same time, Nietzsche states as well that as a biased individual, interaction with
others can help us to pursue knowledge (ibid.). Yet, taking his theory as an argument
for epistemic democracy is not right, as Nietzsche was a strong opponent of
democratic decision-making because he perceived humans as irrational beings
(ibid.). Nietzsche’s approach to knowledge is something that epistemic democrats
appeal to since it means that more people infer a closer approach of the asymptote
that is objectivity. Every individual has a piece of the truth and together people are
able to approach the truth from the aggregation of perspectives. Epistemic
democracy is a means to aggregate these different perspectives on a larger scale and
thus approach a collective decision.
Landemore has also received numerous critiques. First of all there is the question
about what number is considered to be the crucial threshold that turns epistemic
superiority into uncoordinated chaos? And although this is a valid question
Landemore (2013) perceives this as unimportant because it is dependent on case
specific factors. However, within every case there is a limit to the number. This
number could potentially vary when different forms of centred and decentred
deliberation are used (Landemore, 2014). The critique that strikes me most about
Landemore’s theory of epistemic democracy is the seemingly effortless integration of
the wide variety of predictive models within her simple deliberation. A group of
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ideally most distinct individuals gather and naturally are able to optimally
understand and consider each other’s perspective in order to make a decision. She is
not solely talking about homogeneous societies, and formulates her theory for an
ideal type complex modern society (Landemore, 2014). The more complex a society
becomes the more people you would need to mirror the composition of the
population (Landemore, 2013). A more complex society however, possibly
complicates respectful deliberation and enforces polarisation within the deliberation
process (Landemore, 2014). Although Landemore points out that this is not always
the case and the literature is not definitive on this aspect, this leaves a theoretical gap
in the deliberative phase (Ibid.). The gap is an opportunity for improvement and
further research (ibid.).
Likewise, epistemic democrats generally assume a certain goodness and justness
within a state. Condorcet mathematically argues that usually humans only have a
higher than fifty per cent chance of getting it right, which is the foundation of his jury
theorem. He likewise explains that if something happens and this value (the chance
of getting it right) lowers below fifty per cent more people will only decrease the
chances of getting it right. Landemore has a complementary remark of her theory.
She notes that once a state suffers from a society wide bias such as racism, her theory
reverses and the many are dumber than the individual (Landemore, 2012a). She
likewise argues that the reason why there is little empirical evidence for epistemic
democracy is because it is difficult to meet the requirements for ideal political
deliberation (Landemore, 2013). These requirements are for example full-informed
participants, equality amongst them and a certain resistance towards social pressure.
The underlying tendency seems to be that the state needs some sort of stability in
order for this mechanism to work (Landemore, 2014). Landemore, however, does not
provide a mechanism to uncover a state’s instabilities and systemic biases. The
question could be asked whether there is any state that does not suffer from a nation
wide bias. It is therefore not feasible to legitimise a decision made within her
proposed epistemic governing body.
Epistemic democracy, especially with the addition of a selection of a governing body
by lot, has earned a lot of academic attention, but has likewise received scepticism
because of the lack of empiric examples and a continuous debate about forms of
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deliberation (Landemore, 2012a). Collective decision-making as described by
Landemore is rare. This paper therefore suggests to investigates beyond the world of
human societies into the realm of other living species, such as insects. Social
biomimicry is an emerging chapter within the interdisciplinary field of biomimicry
(Fewell, 2015; Grüner et al., 2015). A behavioural biologist might be able to enrich
the field of epistemic democracy and provide insights in collective decision-making
processes of non-human organisms. Therefore next to considering Landemore’s
conception of epistemic democracy I want to provide a dominant understanding of
honeybee collective decision-making (Seeley, 2010). Amongst animal societies there
are only a few insect colonies that match our modern urban society, in resource and
energy distribution as well as scale (Fewell, 2016). The most prominent is the
honeybee swarm (Grüner et al., 2015; Fewell, 2016; Seeley, 2012).
Honeybee collective decision-making
Thomas D. Seeley is professor neurobiology and behaviour (Cornell University, n.d.).
He has devoted his professional career to researching the behaviour of honeybees
and specifically what he calls swarm intelligence (SI) of honeybees (Seeley, 2010). It
is of utmost importance to understand the different lenses used for the researches
used within this paper. Seeley is a biologist and a behavioural scientist and not a
democratic theorist. Biomimicry is the application of biologist insights into a
different field of research and in this case democratic theory. A potential pitfall
within multidisciplinary research is confusion over the definitions. Especially since
Seeley (2010) has applied his insights from his research to the political world though
the lens of a biologist within his book where he summarizes his findings, Honeybee
Democracy.
The title Honeybee Democracy connects animal behaviour to a human made
concept, which literally translated means the rule of the people. The term honeybee
democracy therefore could be seen as a form of anthropomorphism. And although
opposed to the dominant conception of the ruling queen bee, the workers
cooperatively shape the swarm. This being the case, we cannot immediate draw the
conclusion that this would infer the existence of a democracy. There is difference
within homogeneity of the society, cognitive capacities and the importance of the
selves as opposed to the group’s interests (Fewell, 2015; Seeley, 2010). Therefore the
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term democracy or democratic decision-making as used by Seeley will not be used to
define the collective decision-making by honeybees. Instead within this paper a more
informative term will be used, suitable for both humans and animals: collective
decision-making. Each individual honeybee gathers incomplete information with the
aim to aggregate this information and make a decision. To call it a democracy would
only confuse and complicate the discussion.
Common interest is likewise terminology used by both Seeley and Landemore. There
is however a difference between the common interest of a group of humans, and that
of house hunting honeybees. Seeley argues how bees are able to make good collective
decisions in the common interest. The common interest of the honeybee society is
defined as the decision leading to the survival of the swarm on the short and long
term. Within the nest hunting quest that was mentioned in the introduction of this
paper, this infers choosing the nesting site that is most ideal for the hive: A large
space with a small place to enter on a latitude that prevents most other animals from
attacking their potential home (Seeley, 2010). The common interest is strongly
correlated with the individual interest as the swarms’ survival is strongly correlated
with the survival of the individual honeybee. Within epistemic democracy as
described by Landemore the common interest, which leads to the common good, is
not clearly defined. As I interpret the literature, Landemore wants to refrain from
defining the common good. She does this because it is irrelevant for achieving an
epistemic benefit as long as there is a mutual understanding of the common good
amongst the decision-makers (Landemore, 2012b). Besides, she does conclude that a
sovereign can chose to either act in line with the common good or solely out of self-
interest (Althaus et al., 2014; Landemore, 2012b). This implies a duality between
common interests and self-interest as concepts that cannot be aligned. The common
good Seeley describes for the bee swarm is more consequence driven. The human
concept of common good is less based upon consequentialist principles and more
value driven. As both concepts of common interest are linked to the survival or
succession of the individual and the group, I will continue to use common good for
both humans and honeybees.
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The inner workings of the honeybee hive
Honeybees are eusocial insects; insects that have several generations of reproductive
and non-reproductive castes cooperating in nurturing the young (Nowak et al.,
2010). The queen bee is the mother of all the individuals but has no other executive
power over the other bees. Information is dispersed throughout the hive using their
antennas, their body movement and piping signals (Frisch & Lindauer, 1956). The
most prominent example of collective decision-making happens when a daughter
swarm splits off from the mother swarm and has to choose a new nesting site. The
daughter swarm ascends from their original home, flies for a few meters before the
swarm rest and the decision process starts (Seeley, 2010).
The process begins by a selection of nest scouts from the thousand honeybees within
the swarm. The scouts often consist of the older worker bees (the females), which
usually work as foragers for the swarm (Seeley, 2010). This is not surprising, the nest
scouts and the foragers perform very similar tasks with long travel distances and
specific measuring methods. The scouting for alternative nest locations is done over
many hours and sometimes even many days by more than hundreds scouts. Every
scout flies out on its own in order to look for an option that fulfils the list of
requirements it has learned to look for. After they have found a possible option they
report back to the swarm by communicating the location, smell and quality
assessment of the nest site with a dance on the swarm. Note that the information of
any single honeybee is incomplete as it has only searched in one direction and is not
aware of the full range of possible nesting sites (Grüner et al., 2015; Seeley, 2010).
The neighbouring bees follow this so-called waggle dance in order to receive the
information needed to make an independent assessment of the advertised nesting
site (Frisch & Lindauer, 1956). If a neighbouring scout is convinced she will ascend
and travel to the advocated location and make her own independent assessment.
When convincing enough the second scout will also advertise their findings back with
a dance on the swarm. This means that every possible site brought into the mix is
considered and checked by multiple scouts. This lowers the possibility of assessment
errors made by an individual scout (Grüner et al., 2015). This gradually discloses the
phase of consensus building amongst the scouts, consensus; meaning an overall
accepted opinion or decision within the group. Over time fewer new options are
proposed and bees are primarily assessing the nesting sites found by other scouts.
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The dancing honeybees aim to persuade the neutral honeybees to examine their
option of choice. The process of building consensus can be broadly separated into
two parts; the first part consists of the accumulation of a wide variety of possible
nesting sites. In the second part only rarely new options are advertised and the main
task of the bees is to compare and contrast the findings collectively.
Surprisingly the success of a nesting site is not determined by persuading every bee
within the group of scouts to dance for the same location simultaneously; the success
is rather determined by the amount of time any bee is dancing for a nesting site.
Seeley (2010) found that a single scout bee dances for her discovered nesting site
only for a few hours, regardless of the quality of the site. If she succeeds to convince
neighbouring scouts her discovery will be revisited, if not the site is abandoned. This
way the decision process is carried on from first generation scouts to the second-
generation scouts and onward until all the active scouts are dancing for the same
option. This works as a negative feedback loop as unconvincing options are
automatically filtered out. Besides, it leaves room to process good quality nesting
sites that reveal themselves later in the debate because of for example a longer travel
distance (Grüner et al., 2015).
Finally the decision is made once no other nesting site(s) but one is advertised. Yet,
scouts do not have a synoptic overview over the discussion, they solely know what
their direct adjoining bees are advertising. The bees use a quorum in order to
overcome their limitation of not having the cognitive ability to keep count and poll
the dances on the swarm. This infers that about 20 to 30 bees, depending on the size
of the swarm, have to be present at the nesting site for the decision to be made
(Franks et al., 2002). This amount represents a quorum and is therefore a threshold
after which the bees sense that a decision has been made. Once this happens the
scouts return to the swarm and send out a signal. This piping signal will alert the
other bees that the decision is made which leads them to ascend and travel to the
chosen destination (Franks et al., 2002; Seeley, 2010).
The realm of eusocial insects has received increasing interest in the field of social
biomimicry due to their capability to make group decisions with more than hundreds
of individuals. Honeybee swarms, alike ant colonies are often determined as
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superorganisms due to the insect’s exquisite capability to work together as if they are
one (Grüner et al., 2015). Theorists have been exploring the possibility of applying
behavioural patterns of insects to the social sciences. Seeley has attempted to distract
insights from his lifelong work in the field of honeybee behaviour (Seeley, 2010). He
has however only marginally linked his knowledge in this field to behavioural and
political sciences. Grüner, Fietz and Jantsch (2015) have placed the honeybee swarm
on the spectrum of the rational human to the bounded rational and the emotional
human and place the swarm close to the rational human. They argue that humans are
modelled as rational individuals with a rational decision-making process (ibid.). He
additionally introduces the concept of the bounded rational human, which states that
humans make non-optimal choices that are satisfactory as opposed to rational
(Simon, 1990). For a rational decision the decision-maker is in need of full
information, static interests and the capability to overlook all the consequences of a
decision. None of these requirements are feasible according to Simon (1990), which
results in the bounded rational human. From here Grüner et al. (2015) makes the
assumption that we can learn from the bees in order to approach the rational human,
economist are modelling and make better decisions. This could infer that when we
apply the model of the bee swarm we might make better decisions. Fewell (2015) has
investigated the application of Seeley’s (2010) theory and determined the differences
and similarities between insect and human behavioural theories. Regardless of the
simplicity of the bees’ cognitive activity and the obvious presence of a common good
within their colony, their way of decision-making possibly works as a model. Bees
have a homogenous society largely focussed on group success. Human societies,
however, consist of a spectrum of individuals pursuing their own combination of
both individual and group success (Fewell, 2015; Grüner et al., 2015). Still alike
honeybees the human’s individual prosperity is heavily intertwined with the success
of its environment (Fewell, 2015). Although it is acknowledged that the relationship
between individual and group prosperity might be more complex due to the
difference in homogeneity, group cohesion and individualisation, the resemblance
remains (Fewell, 2015; Grüner et al., 2015).
Although direct comparison cannot be achieved due to substantial differences
between human and insect societies, there are some similarities to be touched upon
(Fewell, 2015). I will do this by dividing the decision-making process into three
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phases earlier determined by Landemore, the first being the aggregation of option in
the aggregation phase. The second, the deliberative phase, containing the
distribution of options amongst de decision makers and, at last, the actual decision-
making procedure in the decision phase.
1. Aggregation phase
Aggregation of options within Landemore’s epistemic democracy infers constituting
a decision-making group with much cognitive diversity. This cognitive diverse group
is constituted through a selection by lot, which is both efficient and economically
favourable. This form of representation however implies as well that the decision
body can consist of inexperienced people. Interestingly honeybees alike what
Landemore argues have a selection of decision makers that have no decision-making
experience. As any bee lives less than half a year and a swarm normally choses a new
home site only once a year. Landemore argues that experience is not essential, as
ability is placed third below (1) diversity and (2) numbers. Instead, diversity leads to
a wide range of predictive models that can be applied to the problem (2012).
Landemore’s argument is that applying, as much predictive models to a situation,
will provide the group or governing body with a variety of scenarios, unimaginable
for a single individual. The different perspectives that Nietzsche formulates are, for
humans, represented within the diversity of the group. Amongst the bee scouts the
perspectives are embodied within the search-direction of the individual location
scouts. Every bee has limited information yet as time passes the nest scouts are
capable of collectively disclosing the full range of nesting options in the area. Time
and the capabilities of a single bee to discover all the possible nest sites in the
surrounding region are limited. The time pressure is high because the swarming
phase is dangerous for a hive. Every scout flies out in a different geographical
direction and discovers one or several possible nesting spaces. It is thus of
importance that the bees work together in order to aggregate the nest locations
faster.
2. Deliberation phase
For humans, the most compelling form of distribution of options or predictive
models would be through speech. Landemore proposes to use deliberation in a
manner that lets every individual consider the other predictive models. This is a way
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to test both problem definition and solution proposals to different predicative models
and provide robustness to the decision. Amongst the scout bees the robustness of the
nest location is provided by a mutual inquiry of each nest site. Every scout checks the
allegations of the other scout before she accepts the claim. Landemore pays attention
in her description of deliberation but limits her theory to simple face-to-face
deliberation (2014). The deliberation is centralized in a way that everyone shares
their ideas within a group as opposed to having multiple one to one conversations.
She admits that this infers small groups but sees research perspective for new forms
of epistemically optimal deliberation (Landemore, 2014). For honeybees the
distribution of ideas is through a decentralized network (Grüner et al., 2015). When a
honeybee comes back to the swarm hanging from a branch it excitingly promotes its
found potential nesting site yet only the surrounding bees are able to receive the
information. If one of the surrounding honeybees is convinced it will check on the
nesting site and come back to inform the next surrounding group about the quality of
the nesting site. What is interesting is that honeybees do not reach consensus on the
swarm but reach a quorum at one of the possible options of choice. There is no leader
aggregating the opinions or accounting the dances on the swarm. Humans on the
other hand usually are able to interpret the general opinion of the group within the
debate and the direction it is heading. Being able to account the general opinion is
convenient but it can also discourage honest contribution, aimed at manipulating the
deliberation.
3. Decision phase
The decision on one of the options for political deliberation is often a majority vote
although smaller groups often seek consent (absence of objection) or consensus
(mutual agreement). Honeybees work with a quorum, which implies there is a
threshold on the choice for a nesting site, and when the threshold is reached
(meaning that sufficient honeybees are at the nesting site) the decision is final.
Although Seeley’s conceives majority vote a sign of division, Landemore argues that
the majority vote will cancel out individual faults and biases. Landemore sees
strength in the differences between people because it is able to better internalize the
complexity of politics. Seeley’s sequential analysis of the honeybee movement on the
swarm can here provide insights. Favourable nest locations are passed though the
location scout group, meaning that the scout that discovers the site is not present at
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the location when the quorum is sensed. This differs remarkably from humans’
central decision procedure. Central deliberation and decision-making could
emphasize the perspective of the more outspoken individuals and encourage a
tactical vote.
Throughout the three steps the significant difference between the two ways of
making collective decisions is the framework from which the individuals
communicate and aggregate information. Honeybees communicate through a
decentralized network where one honeybee transfers its information to the next. The
bees act upon a change in this information stream and accordingly know how to
proceed. Communication within an epistemic democracy is centralized (although
Landemore identifies room for different forms of deliberation). The diverse group of
individuals selected by lot gather and consider the perspectives on the issue at hand
and subsequently make a decision. The interesting element of honeybee societies is
that they appear to work together in a self-regulating decentralized system; a
balanced system that manages to execute complex tasks with cognitively limited
individual bees (Grüner, 2015). Each honeybee operates according to a very limited
and simple set of rules. When a threshold is reached the scout starts producing
signals and when the signals resonate the scout ascends (Franks et al., 2002). Or
when the neighbour bee dances vigorously, another scout reads the information from
the dance and when convinced investigate the site. However all these separate
actions interact and almost always guide the swarm into the most fit nesting site
possible. This is at least the system that biologists present to the world from their
perspective. Grüner argues for the investigation of the application of such a
decentralized system into human collective decision-making (2015).
Interestingly, both Seeley and Landemore argue for a form of intelligence within the
group. Seeley is talking about swarm intelligence when he explains how a group of
individual honeybees is able to overcome its individual cognitive deficiency. The
abilities of bees to aggregate information help them to reduce the trade off between
speed and accuracy, which is essential within decision-making. Landemore likewise
argues that intelligence is overcoming one’s own shortcomings. Landemore even
states that collective intelligence is more than the sum of the intelligence of the
individuals within it. This is the potential what she is striving towards and also the
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purpose of the framework she theorized. The framework is her argument for why
democracies have an epistemic edge over other forms of government. Both theorists
define intelligence as the capability of a group to become more than the sum of its
parts. Seeley has witnessed this empirically within the swarm. Landemore potentially
aims to theorize a similar framework but designed for the cognitively diverse human
kind. A systematic analysis might improve most important deliberative phase, as it is
a method to overcome the individual constraints. Thereby it can improve the group’s
intelligence (Meadows, 2008).
System Thinking
Seeing the world as a system is something that originates from multidisciplinary
research but is commonly used in environmental and behavioural sciences
(Haraldsson, 2004). System thinking works from the assumption that all of the
scientific disciplines are non-linear. This means that any linear process discovered or
researched happens within a context within a field of other interacting processes
(Meadows, 2008). The interaction between the elements of a system can produce
feedback loops, a loop of elements that once a change is made in one element all the
other elements start enforcing or stabilizing the change made earlier. The loop that
enforces a change in information is called a reinforcing feedback loop. Contrary, the
loop that stabilizes the change made earlier is called a balancing feedback loop. These
feedback loops present themselves in many variations; they can be either dominant
or inferior throughout working of the system (Meadows, 2008). These interactions
are complicated to comprehend for a human mind because we are used to think in
linear relations (Meadows, 2008). As humans, we can only keep an eye on several of
the numerous variables within a system. We either have biased information, which
leads to false conclusions, or we have relatively accurate information but draw the
wrong conclusion (Meadows, 2008). This phenomenon of making acceptable
decisions with little information is theorized as bounded rationality (Simon, 1990).
Systematic analysis is a means to overcome our own cognitive limitations alike the
wild bee (Grüner et al., 2015). This cognitive limitation is our bounded rationality
and Grüner et al. (2015) argues that humans can come closer to rational, good
decisions when we look at the honeybee swarm. The honeybee swarm is a simplified
version of individuals within a complex and ever changing world. Thinking in
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systems is a way to visualize, organize and integrate information in order to
understand patterns and relations within complex problems (Haraldsson, 2004).
Landemore has likewise proposed a systemic approach to deliberation as a possible
enrichment of epistemic democracy (2014). These deliberative democrats propose a
decentred system of smaller deliberative groups in order to divide the requirements
for optimal deliberation over several institutions (Chambers, 2017; Mansbridge et al.,
2012). System theory within this thesis is, however, used for its epistemic benefit. It
is used as a means to gather and organize diverse interpretive models as opposed to
deliberation groups. In the following paragraphs, system theory will provide an
epistemic answer to the information gap within the deliberation phase of
Landemore’s theory.
Meadows determines three things necessary for a system: elements, interconnections
and a purpose or function (2008). Within the swarm looking for a nesting site, the
individual bees are the elements. The interconnections are the rules according to
which they act. The purpose of the swarm can largely be seen as survival,
corresponding to the common interest that was described earlier. But when you
reduce the system solely to the swarming period, the purpose of the system is to find
the best home in their reachable surrounding. Within a beehive the honeybees that
advertise for favourable nesting sites produce reinforcing feedback loops as more and
more bees start dancing and advertising the site which leads to exponential growth of
bees dancing for that site. Complementary, bees dancing for a less favourable site will
dance less convincing which produces a stabilizing or balancing feedback loop as
perhaps only one bee is convinced to check the medium quality nesting site but feels
not passionate enough to advertise it which cancels out the less optimal decision
option. Honeybees live in societies with more than thousands of individuals who
independently need to assess their course of action based upon what is needed within
the hive. These basic rules internalized by the bees help them to deal with this
complexity and perhaps can enrich humans to deals with our complex societies
(Fewell, 2015).
Of course humans have societies that are much more dynamic and every individual
does not act according to a standard set of rules. We are much more likely to change
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our behaviour in favour of our social relationships (Fewell, 2015). However ideally
governments are able to construct a balanced society where resources are naturally
distributed and every individual is encouraged to develop and grow. We can see this
for example within many progressive tax systems. When a household has a
consistently increasing income, they will have to pay relatively more taxes since they
likely profited from the current governmental and societal framework. This income
tax consequently can be utilized to improve the societal framework in order to raise
the lowest societal class (Ministry of Finance, 2020). A government is created to
interfere with society only when it appears to fall out of balance, not when it is
completely balanced. Liberals and socialists define out of balance in a different way,
but the general principle is widely accepted among theorists (Schwartz, 2010). Seeing
a state as a deliberative system has similarities to Mansbridge et al. (2012), but
without the focus on the epistemic benefit. There is complexity within human
societies that is much harder to grasp and leaves much room for chance compared to
honeybee societies. The message here is that complexity can seem overwhelming but
by visualising the system it can become easier to understand and comprehend
complexity (Meadows, 2008).
The approach of thinking in systems can fulfil an epistemic addition to deliberation
as described within Landemore’s epistemic democracy. The aggregation and
integration of predictive models as described by Landemore can be more effective
when theorized within the framework of system thinking. The framework can assist
the group to grasp the non-linearity and the interconnection, difficult to disclose in
either written or spoken language. Language is constructed within a linear essence,
building on logic and consequential relationships (Meadows, 2008). Nietzsche states
that knowledge is constructed from dialogue; system analysis can be seen as an
approach to deepen dialogue. Visualising language by the means of a system helps to
overcome this shortcoming of spoken and written language. It is difficult to filter out
the differences in deliberative talent amongst the individuals in an open deliberation.
There is pluralism in personalities, which can lead to an unequal consideration of the
insights brought to the table by the individuals. By visualising the system the chances
are higher that every variable is actively considered within everyone’s final individual
vote. This addresses the question many theorists have appointed (Landemore, 2014)
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To summarise, using a system to integrate the predictive models of the members
present in a decision group does not reject the theory of Landemore. It rather builds
upon her theory and perceives thinking in systems as a way to epistemically enrich
the deliberation phase of Landemore’s theory. System theory can face the
information gap in the deliberative phase as it addresses both pluralism and the
human cognitive limitation. Alike the honeybee, system theory helps to overcome
individual cognitive limitations and approach the epistemic rational choice.
Thinking in systems has the potential to uncover hidden system traps and system
malfunctions. System mechanisms that inevitably lead to problematic escalating
behaviour whether it is a race to the bottom or destructive growth can be uncovered
(Meadows, 2008). Besides it trespasses the issue of blame and aims at understanding
issues such as institutional racism, climate policy and poverty (idem.).
Let me explain what the application of system thinking might look like. The decision
group has to understand the intention and structure of the decision making process,
however this is proportional to explaining what respectful deliberation entails. The
group is informed about the way a system can be constructed, the purpose, elements
and the interconnections. The purpose of epistemic democracy is to make a decision
that benefits the common good. Further the issue at hand is determined, from there
on elements are (visually) added to the system in order to deepen the understanding
of the systematic problem behind the visual symptoms.
There are many formats to portray a system, ranging from theoretical to
mathematical (Meadows, 2008). Further research might dive deeper into the
pragmatics of system thinking. For now the causal loop diagram functions as an
example, as it is the simplest version and most suitable for qualitative data instead of
quantitative research. A causal loop diagram maps the elements and determines the
direction and character of the relations between them. It can be used to analyse the
workings of policies or separate decisions made. Usually arrows are marked with a
plus or a minus, which indicates either a positive or a negative correlation. When
variables are connected in a circle with arrows pointing the same direction we are
speaking about a feedback loop. The plusses and minuses indicate the nature of the
feedback loop. Zero or an even amount of minuses indicates a reinforcing feedback
loop. An uneven amount of minuses indicate a balancing feedback loop. The causal
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loop diagram is an accessible approach to system theory, easily explained to the
decision-making group.
Another important factor within system analysis is the integration of delays;
especially economists tend to neglect the effect of a delay within a system. Everything
takes time, it takes time for information to be transferred, and it also takes time for
information to be processed. It takes time for money to be transferred and for norms
to be changed. When a delay occurs within a system the output of the system starts to
oscillate. Oscillation is not necessarily a negative effect but does influence the
behaviour of the system.
A great difficulty with this method is setting the limit of the system. Systems for
political use are supposed to be an abstract reflection of the relevant part of the state.
This requires systems to find the balance between too abstract and too detailed. A
system too abstract loses it relevance to real life decisions. A system too detailed loses
its relevance because the chaos makes it impossible to draw any conclusion for real
life. Setting the limit of a system is therefore of great importance for the value of a
system. It is important to understand however that boundaries within a system are
man made for functional purposes. When we look at reality there are not much actual
boundaries and systems are often interconnected with other systems (Meadows,
2008). One might argue that this difficulty, essential to system thinking, is a
challenge for using systems all together. Which is a valid point to discuss since this
proposal is meant to improve deliberation, not complicate it. The representatives are
however selected by lot and have not been operating within the political sphere their
entire life. This aspect gives them an advantage and a disadvantage of having no
prior experience. A disadvantage manifests because of the lack of a learning curve,
the participants are not able to learn from past decisions made with the identical
group. Landemore (2012b; 2013; 2014) addresses this objection to her theory in
several papers. The advantage of having no prior experience is a decrease of
prejudices towards groups, institutions and mechanisms. In the case of drawing
boundaries it is rather useful. People mentally accustom to boundaries they are
interacting with however for different types of issues a different boundary setting is
required. This gives the inexperienced an advantage over an experienced group of
representatives.
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Conclusion
Humans are learning much from the natural world as they have been, for a long time.
Honeybees present us with a form of collective decision making that surprises and
astonishes. Bees are able to aggregate information collected by more than hundreds
of bees and find the option best fit for their swarm within days. Hereby they are
capable of overstepping their individual cognitive shortcomings and work together in
a coordinated, efficient and effective system. Humans have likewise known the
fundaments of epistemic theory since the emergence of democratic theory in historic
Athens. Aristotle first argued for the intelligence of the group over the knowledge of
the individual. Ever since epistemic theory has developed, Landemore has provided
the world with an appealing proposal to fully exploit the wisdom of the crowd. She
argues that for the epistemic benefit to exhibit itself, cognitive diversity is critical.
She bases this on the Diversity Trumps Ability Theorem. According to Landemore,
cognitive diversity will provide the decision-making process with widely varying
predictive models, internalized in the diverse individuals. In order to easily establish
a high level cognitive diversity she proposes to use political representatives, selected
by lot as opposed to a regular election used in the current system. Following she
describes how inclusive and respectful deliberation results in a collectively wise
choice, leading to the common good. The deliberative phase is where honeybee
wisdom can enrich the field of epistemic democracy. It is also the field that
Landemore proposes as an area of further research due to issues of pluralism and the
limitations of the human cognition. The system that bees appear to interact with can
help humans to map and relate the intelligence established from the predictive
models present within the group. Likewise Fewell and Grüner et al. propose the
opportunities for honeybee intelligence to refine social sciences. System thinking is
an approach to visualize the interconnected variables that shape a problem. It can be
a means to collectively acquire and shape knowledge in the tradition of Nietzsche’s
perspectivism. This is necessary in order for a group to overcome their own cognitive
limitations alike the wild honeybees. Cognitive limitations such as our human
bounded rationality, the human difficulty to keep track of multiple variables
simultaneously and the difficulty we face to to comprehend non-linear relationships.
Using system theory as a foundation for democratic deliberation might enhance the
epistemic edge democracy has over other forms of government. Within these
systems, reinforcing and balancing feedback loops can be defined which can help to
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