Self-organization and time-stability of social hierarchies

Abstract

The formation and stability of social hierarchies is a question of general relevance. Here, we propose a simple generalized theoretical model for establishing social hierarchy via pair-wise interactions between individuals and investigate its stability. In each interaction or fight, the probability of “winning” depends solely on the relative societal status of the participants, and the winner has a gain of status whereas there is an equal loss to the loser. The interactions are characterized by two parameters. The first parameter represents how much can be lost, and the second parameter represents the degree to which even a small difference of status can guarantee a win for the higher-status individual. Depending on the parameters, the resulting status distributions reach either a continuous unimodal form or lead to a totalitarian end state with one high-status individual and all other individuals having status approaching zero. However, we find that in the latter case long-lived intermediary distributions often exist, which can give the illusion of a stable society. As we show, our model allows us to make predictions consistent with animal interaction data and their evolution over a number of years. Moreover, by implementing a simple, but realistic rule that restricts interactions to sufficiently similar-status individuals, the stable or long-lived distributions acquire high-status structure corresponding to a distinct high-status class. Using household income as a proxy for societal status in human societies, we find agreement over their entire range from the low-to-middle-status parts to the characteristic high-status “tail”. We discuss how the model provides a conceptual framework for understanding the origin of social hierarchy and the factors which lead to the preservation or deterioration of the societal structure.

Full text

RESEARCH ARTICLE
Self-organization and time-stability of social
hierarchies
Joseph HickeyID*, Jo
¨rn Davidsen
Complexity Science Group, Department of Physics and Astronomy, University of Calgary, Calgary, Alberta,
Canada
*joseph.hickey@ucalgary.ca
Abstract
The formation and stability of social hierarchies is a question of general relevance. Here, we
propose a simple generalized theoretical model for establishing social hierarchy via pair-
wise interactions between individuals and investigate its stability. In each interaction or fight,
the probability of “winning” depends solely on the relative societal status of the participants,
and the winner has a gain of status whereas there is an equal loss to the loser. The interac-
tions are characterized by two parameters. The first parameter represents how much can
be lost, and the second parameter represents the degree to which even a small difference of
status can guarantee a win for the higher-status individual. Depending on the parameters,
the resulting status distributions reach either a continuous unimodal form or lead to a totali-
tarian end state with one high-status individual and all other individuals having status
approaching zero. However, we find that in the latter case long-lived intermediary distribu-
tions often exist, which can give the illusion of a stable society. As we show, our model
allows us to make predictions consistent with animal interaction data and their evolution
over a number of years. Moreover, by implementing a simple, but realistic rule that restricts
interactions to sufficiently similar-status individuals, the stable or long-lived distributions
acquire high-status structure corresponding to a distinct high-status class. Using household
income as a proxy for societal status in human societies, we find agreement over their entire
range from the low-to-middle-status parts to the characteristic high-status “tail”. We discuss
how the model provides a conceptual framework for understanding the origin of social hier-
archy and the factors which lead to the preservation or deterioration of the societal structure.
1 Introduction
Animals, including humans, form social hierarchies [1–5]. How these hierarchies form and
what makes them remain stable over time is a central question across many different fields. In
the humanities, social and political theorists have studied the origin of class structures and the
conditions under which these structures are preserved or change [6–9]. Archaeologists and
other researchers from diverse fields study the factors that lead to the collapse of civilizations
[10,11]. Anthropological research has focused on the roles of norms, sanctions, and coopera-
tive behaviour in creating and maintaining hierarchy [12–14]. In the biological sciences,
PLOS ONE | https://doi.org/10.1371/journal.pone.0211403 January 29, 2019 1 / 30
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OPEN ACCESS
Citation: Hickey J, Davidsen J (2019) Self-
organization and time-stability of social hierarchies.
PLoS ONE 14(1): e0211403. https://doi.org/
10.1371/journal.pone.0211403
Editor: Lazaros K. Gallos, Rutgers The State
University of New Jersey, UNITED STATES
Received: December 7, 2017
Accepted: January 11, 2019
Published: January 29, 2019
Copyright: ©2019 Hickey, Davidsen. This is an
open access article distributed under the terms of
the Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: Canadian income
data is available from Statistics Canada <https://
www.statcan.gc.ca/eng/help/microdata>. USA
income data is available from the Integrated
Public Use Microdata Series: <https://usa.ipums.
org/usa/>.
Funding: JH was financially supported by the
Natural Sciences and Engineering Research
Council of Canada through a Canada Graduate
Scholarship. JD was financially supported by the
Natural Sciences and Engineering Research
Council of Canada through a Discovery Grant. The
funders had no role in study design, data collection

researchers have questioned whether hierarchy emerges primarily from differences in intrinsic
qualities of individuals (e.g. physical strength, intelligence, or aggressive tendency) or as a self-
organizing process in which a hierarchy arises as a result of many interactions between the
members of the society [15–18].
From a high-level perspective, a fundamental question arises: Can a stable or long-lived
hierarchical structure occur entirely by self-organization, based solely on inter-individual
interactions, modeled as independent pair-wise “fights”? And, if so, what are the typical struc-
tures of hierarchy, and what are the characteristic times of formation and evolution of the said
structures?
“Winner-loser” models are a class of mathematical models that have been used to study the
self-organization of social hierarchy in biology [18–29] and economics [30–34]. In these mod-
els, individuals are characterized by a property, such as “strength”, “resource holding poten-
tial”, or “wealth”, that determines the individual’s position in society (in the following, we use
“strength” as a generic term for this property). Pairs of individuals come into contact and
engage in an interaction (or “fight”). The fight has a winner and a loser, where the winner
experiences a gain in strength, and the loser loses strength. The models have two basic rules:
one that determines who wins in a given fight, and another that determines the amount of
strength gained or lost in a fight. The distribution of strength, which changes as individuals
interact with each other, represents the societal structure resulting from the model. While
stable societal structures have been analyzed in previous studies of winner-loser models, the
time evolution and intermediary, potentially long-lived societal structures have been mostly
neglected. Here, we aim to close this crucial knowledge gap.
To do so, we construct a generalized winner-loser model in which we intend the strength
property to represent societal status. The amount of status gained by the winner and lost by
the loser of each fight is proportional to the pre-fight status of the losing individual. We define
a probability for winning that is determined by the relative statuses of the two competitors,
modulated by a parameter spanning a continuous range of degree of authoritarianism from
redistributive (lower-status opponent always wins) to totalitarian (higher-status opponent
always wins). The latter modulation for winning contains previous models as special cases at
specific values of the authoritarianism parameter, and allows a more general description of the
dynamics. Over a large range of parameters and excluding these special cases, we find the
emergence of long-lived intermediary societal structures (distributions of societal status) for
the first time. Establishing the existence of these long-lived structures—which can give the illu-
sion of a stable society—and the relationship between the characteristic time of their evolution
and the model parameters is one of the main contributions of our study.
To demonstrate the relevance of our generalized model and the long-lived structures, we
analyze real-world data. Specifically, we compare data from observational studies on wins and
losses in animal interactions with the results from simulations of our model, and we compare
the distributions of societal status produced by the model with real-world social hierarchies.
To make the latter comparison, we use proxies for societal status in large social groups. In both
cases, the real-world data are consistent with our model. Specifically, in our model, long-lived
intermediary societal structures (distributions of societal status) arise independent of whether
any pair of individuals are equally likely to interact or not. In the latter case, however, status
distributions with more complex shapes consistent with the household income proxy emerge.
We are able to fit the simulated status distributions to USA household income data with good
agreement. To our knowledge, this is the first model that produces the two-part structure of
the proxy distribution by self-organization based solely on interacting individuals.
The model is presented in section 2 and an extended version of it in which similar-status
individuals interact more frequently than individuals with large differences in status is
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and analysis, decision to publish, or preparation of
the manuscript.
Competing interests: The authors have declared
that no competing interests exist.

presented in section 2.1. Details about the shapes of the status distributions and their evolution
in time are presented in section 3. Comparison of model results to data from real societies is
contained in section 4, where we consider data on agonistic interactions in non-human ani-
mals in section 4.1 and proxies for societal status in large social groups (social insects and
humans) in section 4.2. The article concludes with a summary of results and some comments
regarding future research directions.
2 Definition of the model
Winner-loser models have been constructed using many variations of the rule determining
who wins the fight and the rule determining the amount of strength gained or lost in a fight,
where the particular formulation chosen for each rule depends on the system under study.
In the rule determining the amount of strength gained and lost in a fight, two formulations
have been applied previously. In one version (“additive” rule), the effect of fighting on an indi-
vidual’s strength accumulates additively, for example, by the addition or subtraction of a fixed
increment of status [20–25]. In an additive rule, the amount of strength gained or lost in a
fight does not depend on the current value of either individual’s strength. This means that the
amount of strength won or lost in a fight is always the same, regardless of the strength of one’s
opponent.
The other version of this model rule is a “multiplicative” one. Here, the amount of strength
gained or lost is proportional to the strength of one of the individuals involved in the fight,
such that effect of fighting accumulates multiplicatively [27,31–33]. Defeating a strong oppo-
nent produces a large increase in strength, whereas defeating a weak opponent produces a
small increase in strength. It is clear from animal behaviour studies that wins against high
ranking individuals increase the rank of an individual more than wins against low ranking
individuals. In this case, a multiplicative rule is therefore more realistic than an additive rule,
in which it is no more advantageous for an individual to defeat a strong rather than a weak
rival. Moreover, whether an additive or multiplicative rule is used leads to substantially differ-
ent distributions of strength [19]. For example, in many models with additive rules, strength
becomes distributed such that individuals of adjacent ranks are separated by the same amount
of strength. In multiplicative models, on the other hand, highly skewed distributions can
result, and such multiplicative processes have been proposed as a common underlying cause of
observed inequalities in natural and social systems [35,36].
Here, we implement a formulation of the multiplicative rule in which the amount of
strength won or lost is proportional to the pre-fight status of the losing individual (“loser
scheme”). In another formulation that has been used in several econophysics models [32–34,
37–39], the amount of strength won or lost is proportional to the pre-fight status of the weaker
individual, regardless of who wins or loses (“poorer scheme”). The loser scheme formulation is
more realistic in the context of dominance hierarchies, because upset victories, in which the
lower-strength individual in the pair wins, produce large rewards for the winner and large pen-
alties for the loser. For example, in primate dominance hierarchies, only a small number of
repeated defeats of a higher-strength individual by the same lower-strength individual are
required for their rankings to be reversed [40]. This scenario is captured by the loser scheme
but not by the poorer scheme.
With regards to the rule determining which individual wins in a pairwise fight, two primary
formulations have been applied: one in which the probability that the stronger individual
wins depends on the difference in the strengths of the two individuals [20–23,26,33], and one
in which this probability depends on a ratio of the strengths of the two individuals [27–29].
We focus on the latter of these two formulations. This choice is related to our choice of the
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multiplicative rule for the amount of strength won or lost in the fight. In a multiplicative rule,
large absolute differences in strength typically exist among individuals of similar rank, at the
top-end of the strength distribution. Therefore upsets, in which the lower-strength individual
defeats the higher-strength individual, become very unlikely or impossible at the top-end of
the distribution of strength when the probability of winning depends on the difference in
strengths of the two individuals. When the probability of winning depends on a ratio of the
statuses of the two individuals, upsets tend to be more likely, especially between two high-
strength individuals separated by a large absolute amount of strength.
Conversely, in a model with an additive rule for the amount of strength won or lost, it may
well be appropriate for the probability of winning to depend on the difference in strengths of
the two individuals, since the status of an individual is equal to the difference in the number of
times the individual has won and lost fights. However, especially in more complex animals, it
is unrealistic to assume that the probability of winning is based on a tally of the number of
fights won and lost, as this information is unavailable to the individuals involved in the fight.
Rather, a more realistic assumption is that a psychological process occurs in which the two
individuals make a rough comparison of one another’s relative strengths, where this compari-
son influences each individual’s probability of winning via characteristics such as confidence,
willingness to take risks, and aggressiveness [2,27]. This assumption is supported by psycho-
logical research showing that perceived change of a physical stimulus depends on the relative
rather than the absolute change in the stimulus [19,41].
Our specific model is constructed as follows. We consider a system of Nindividuals, each
possessing a strength property, S, that determines the individual’s societal position. We intend
Sto represent the societal status of the individual, and accordingly we refer to “status” rather
than the generic term “strength” in the remainder of this article. At each step in the simulation,
a pair of individuals is randomly selected, and engages in a “fight”. The probability, p, that the
higher-status individual wins the fight is expressed as a function of its status, S
1
, and that of its
(lower or equal status) opponent, S
2
:
p¼1
1þ ðS2=S1Þa:ð1Þ
When α= 1, the probability that either individual wins is equal to the ratio of its own status to
the sum of its and its opponents statuses. However, as αis tuned to values other than 1, the
advantage held by the higher-status individual changes (Fig 1). As α! 1, the higher-status
individual is virtually guaranteed to win, regardless of how strong its opponent is. On the
other hand, when αis small but positive, the higher-status individual only has a large advan-
tage in fights against opponents with much lower status. When αis negative, 0 p<0.5, indi-
cating that the lower-status individual in any given fight is more likely to win. The parameter α
thus generalizes previous modeling approaches, by allowing the probability for winning a pair-
wise fight to be continuously adjusted between end-points where the lower-status individual
always wins (α=−1) and where the higher-status always wins (α=1).
To interpret the societal meaning of the parameter α, we note that the probability pdepends
on the relative statuses of the two individuals. This means that as long as the ratio S
2
/S
1
is con-
stant, and given a constant value of α, the probability, p, that the higher-status individual will
win is constant, independent of the absolute values of S
1
and S
2
. In a general sense, the proba-
bility that a high-status individual will win in a fight against a medium-status individual is
the same as the probability that a medium-status individual will win in a fight against a low-
status individual. If having a higher societal status can be considered as having a higher level
of “authority” in a hierarchical society, then the parameter αrepresents the degree to which
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there is deference to authority in the society or, in other words, the society’s overall level of
“authoritarianism”.
Next, we explain the rule determining the amount of status transferred from loser to winner
following each fight interaction. Let S
W
be the before-fight status of the winner of the fight,
and S
L
the before-fight status of the loser. Following the fight, a portion Δof the loser’s before-
fight status is transferred to the winner, such that
S0
W¼SWþD
S0
L¼SLD;
where the primed quantities represent after-fight statuses. In our model, the amount of status
transferred, Δ, is equal to a proportion of the before-fight status of the individual who loses the
fight. That is, Δ=δS
L
, where δis a fraction between 0 and 1. This gives us:
S0
W¼SWþdSL
S0
L¼SLdSL;ð2Þ
This rule for the amount of status transferred has realistic implications from the perspective
of formation and maintenance of social hierarchy, because it means that upsets (in which the
Fig 1. Probability that higher-status individual wins in a pairwise fight. The probability p(S
2
/S
1
) (Eq 1) is shown for different values of α. Solid lines
correspond to α>0 and dash-dotted lines to α<0.
https://doi.org/10.1371/journal.pone.0211403.g001
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lower-status individual defeats the higher-status individual) produce large rewards for the win-
ner and large penalties for the loser.
The two rules contained in Eqs 1and 2constitute our “original” (two-parameter) model
that is the main focus of this work. We note that special cases of Eq 1 were investigated in pre-
vious work. Specifically, the case when p= 0.5 in all fights, regardless of the statuses of the two
individuals (α= 0 in our model) and the case when p= 1 in all fights (α=1in our model)
were investigated in a model that uses the same rule as our Eq 2 [31]. The special case when
α= 1 has been used in other winner-loser models, but never in conjunction with a multiplica-
tive rule like our Eq 2. The novel parameter αthus allows us to generalize previous models and
opens a previously unexplored region of parameter space. We also present a simple extension
to our two-parameter model, in the following section.
2.1 Model extension: Restricting fights between individuals with large
differences in status
One of the main assumptions of winner-loser models based solely on the two categories of
rules described in section 2 is that any pair of individuals are equally likely to interact, regard-
less of their strengths. Some biologically-oriented winner-loser models have included mecha-
nisms that adjust the interaction probability of individuals based on their spatial positions or
on their strengths. For example, in Ref. [27], each individual decides whether to engage in a
fight by comparing the ratio of its strength to its opponent’s strength with a threshold; in
Ref. [29] individuals move in a spatial territory and interact if they are within visual range of
one another; and in Ref. [21], individuals interact with a probability equal to the product of a
function of their strengths, such that stronger individuals interact more frequently than weaker
individuals. In a similar vein, we can extend our model by implementing a third model rule
under which pairs of individuals with large differences in status fight less often than similar-
status individuals. Unlike other rules that control the probability that two individuals interact,
our rule allows all individuals with similar statuses to interact frequently, while also reducing
the frequency of interactions (and thus the exchange of status) between individuals with large
differences in status.
These are needed realistic features, because evidence from studies of hierarchies in animal
groups suggests that a large proportion of the status-determining interactions experienced by
high status individuals pit these individuals against “challengers” who themselves have higher
than average status [40,42,43]. Meanwhile, low status individuals tend not to challenge high
status individuals, such that low status individuals are more likely to interact amongst them-
selves [43]. Similarly, humans are more likely to interact with members of their own social
classes, especially at the extremes of the social class spectrum [44], and residential segregation,
which impedes interactions between members of different social classes, is considered to be a
primary factor in the creation and exacerbation of social stratification [45].
Specifically, our extended model introduces two additional parameters. First, following
from observations that high status individuals are more likely to engage in fights with similarly
high status challengers, the new rule imposes that two selected individuals only engage in a
fight if their absolute statuses are separated by not more than a threshold amount Z�
S. Here,
η0 is a new parameter that sets the size of the threshold relative to the (conserved) average
status of the system, �
S, which is a natural reference point for the threshold position. Secondly,
notwithstanding the above-noted observations regarding the higher frequency of interactions
between similar-status individuals, animal behaviour studies also show that high status indi-
viduals do interact with low status individuals at times. This occurs, for example, through
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seemingly random acts of aggression which may play an important role in maintaining hierar-
chical rank-ordering [46]. For this reason, a realistic model should not exclude the possibility
that fights between high and low status individuals will occasionally occur. Therefore, regard-
less of the result of the threshold criterion, the fight between the two selected individuals takes
place if r< �, where 0 �1 is a new parameter and ris a random number such that 0 
r<1.
In summation, the new rule to limit interactions based on the statuses of the two competi-
tors can be stated as follows: two individuals are selected at random, and they fight if S1S2
Z�
SOR r�. We note that, in the implementation of the simulation, this rule does not change
the probability with which any two particular individuals are selected from the population, but
only adds a threshold criterion to decide whether or not the fight occurs between the selected
pair.
3 Time-evolution and structure of status distributions
In the society envisioned in the model, pairs of individuals interact such that they gain or lose
societal status in accordance with the rules described in section 2. As interactions take place,
and status is exchanged between the members of the society, a distribution of societal status
takes shape. Our primary goal in this study is to investigate the structure of these status distri-
butions and how they evolve in time. In this section, we therefore investigate the shapes of
the status distributions as functions of the model parameters δand α(section 3.2), and then
quantify their time evolution in terms of two characteristic times (sections 3.3-3.5). Before pre-
senting these results, we first (section 3.1) establish how time is defined in the model. This
introduces the first characteristic time of the system’s evolution, which gives us a basis on
which to present the results in the following sections. Please note that in the directly following
sections we focus on the original (two-parameter) version of the model first as many features
are qualitatively the same as for the extended version of the model. The features specific to the
extended model are then discussed in section 3.6.
3.1 Definition of time and the characteristic time τ
1
In order to discuss the shapes and time-evolution of the status distributions formed by simula-
tions of the model, we must establish how time is defined. For simplicity and without loss of
generality, we model the pairwise interactions as instantaneous such that they can be described
as sequential events. We consider that one unit of time has passed once all members of the
society have, on average, engaged in one pairwise interaction or fight. Under this definition of
time, the rate at which an individual participates in a fight is an intrinsic frequency of the sys-
tem, independent of system size, N, where Nis the number of individuals in the system. Time,
t, is therefore defined as t= 2t0/N, where t0is the number of fights that have occurred since the
initiation of the simulation, and the factor of 2 comes from having each interaction involve
two individuals. One unit of time is equal to N/2 fights.
Previous work by Ispolatov et al. [31] shows the existence of a characteristic time in a model
that is mathematically equivalent to our original (two-parameter) model in the case where
α= 0. An analytic solution for the time evolution of the variance of the status distribution
(wealth distribution, in Ref. [31]) was found to be as follows:
M2ðtÞ ¼ d�
S
1d1edð1dÞt
 �;ð3Þ
where the variance (second central moment) M
2
(t) can be calculated directly from the status
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distribution:
M2ðtÞ ¼ PN
i¼1ðSiðtÞ  �
SÞ2
N;ð4Þ
where S
i
(t) is the status of individual iat time t, and �
Sis the (conserved) average status.
Similar to Eq 3, higher moments of the status distribution converge to constant values, and
the status distribution attains a steady state. Eq 3 therefore shows that the variance approaches
a steady-state value of M2¼d�
S=ð1dÞat large times and that the approach to the steady-
state is characterized by a time constant equal to (δ(1 −δ))
−1
.Eq 3 can be re-written in a form
that is useful for our purposes:
M2¼c1ð1et=t1Þ;ð5Þ
where c
1
and τ
1
are generally functions of δand α. In the following, we use the symbols ^
c1and
^t1to represent these functions when α= 0, such that ^t1¼ ðdð1dÞÞ1and ^
c1¼d�
S=ð1dÞ,
as per Eq 3. Fig A1 in S1 Appendix demonstrates that our definition of time corresponds to
how time is defined in the analytical result in Eq 3.
3.2 Overview of status distributions produced by the model
In this section, we present the shapes of the societal structures (distributions of status) that
emerge in simulations of the model. We begin by setting α= 0 (p= 0.5 for all fights, regardless
of the statuses of the competitors as per Eq 1) because, in this limiting case, there always results
a stable steady-state status distribution as we show in the following.
In Fig 2, we show graphs of steady-state distributions for several values of δwhen α= 0. As
can be seen, the shape of the steady-state distribution varies from rather egalitarian for small δ
(e.g. δ= 0.04: all individuals have close to the average status) to highly unequal (e.g. δ= 0.81:
most individuals have very low status and small portion of the population has very high status).
As was noted in section 3.1, when α= 0, our model is mathematically equivalent to the model
of Ispolatov et al. [31], who showed that the tail of the distribution decays exponentially for all
values of δ.
The inset of Fig 2 shows the time evolution of the variance of the status distribution, M
2
,
which provides a measure of the level of inequality of the society. Larger values of δgive rise to
larger steady-state value of M
2
. As expected from Eq 3,M
2
approaches a steady-state plateau
with a value of ^
c1. The skewness γ=M
3
/(M
2
)
3/2
, where M
3
is the third central moment of the
distribution, also arrives at a steady-state plateau at large time (dashed grey lines in inset of
Fig 2). This plateau in M
2
and γindicates that the shape of the status distribution is unchang-
ing in time. The plateau in γ
2
is equal to four times that of the plateau in M
2
, as can be shown
by solving for the third moment following the approach presented in Ref. [31].
Fig 3 shows distributions of societal status obtained for a fixed value of δand for various
values of the authoritarianism α. The curve for α= 0, δ= 0.14 from Fig 2 is reproduced in Fig
3, along with the inset, showing that M
2
(t) undergoes an initial transient period before arriving
at a plateau value for times t^t1.
However, for large values of α>0 (α= 0.6 and α= 0.8), the inset of Fig 3 shows a rapid
increase of M
2
(t) that continues beyond the initial transient period. The status distributions
are rapidly evolving (“running away”) toward a totalitarian end-state in which a single individ-
ual possesses virtually all of the societal status of the system and all other individuals have
status approaching zero. The shape of the status distribution changes rapidly during this evolu-
tion, becoming more and more skewed with time. Section E in S1 Appendix contains figures
showing how the status distributions evolve over long times, and Section F in S1 Appendix
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contains a proof that only one individual with non-negligible status remains in the totalitarian
end-state.
For smaller values of α>0 (α= 0.2 and α= 0.4), M
2
(t) (and therefore the shape of the status
distribution) appears to be virtually unchanged in the time following the initial transient
period shown in Fig 3. Yet, as we show below (sections 3.3 and 3.4), M
2
(t) does increase with t,
albeit much more slowly, such that the status distributions can be considered to be in a long-
lived state, where the shape of the distribution can change so slowly that it is essentially
unchanged over sufficiently short observation times.
Fig 3 also shows the status distributions that arise when α<0. For this region of parameter-
space, the lower-status individual has a higher probability of winning the fight than the higher-
status individual. Our numerical simulations show that the distributions are in steady-state (as
for α= 0) and become more egalitarian (smaller M
2
) as αis decreased while δis held constant.
The plots in Figs 2and 3were obtained using an “egalitarian” initial condition, in which all
individuals have an initial status of S= 1. Figs B1 and B2 in S1 Appendix show that the same
distributions that arise under an egalitarian initial condition also arise when the system is pre-
pared in a “uniform” initial condition in which the initial statuses are randomly selected from
a uniform distribution with �
S¼1.
3.3 Long-lived behaviour
In this section we quantify the time evolution and the long-lived behaviour of the status distri-
butions produced by the model by examining the evolution of the variance of the status distri-
bution, M
2
(t), when α>0 (Fig 4a–4c).
Fig 2. Shape of status distributions as function of δ, with α= 0. Distributions range from more egalitarian (e.g. δ=
0.04: all individuals have close to the average status) to highly unequal (e.g. δ= 0.81: most individuals have very low
status and small portion of the population has very high status). Inset of (a): plateau in M
2
(coloured) and squared
skewness (grey) indicate that status distributions are in steady-state. Larger values of δcorrespond to larger steady-
state value of M
2
, such that M
2
provides a measure of the level of inequality of the society. Distributions obtainedafter
simulating up to time t¼64 ^t1.N= 10
5
,n
r
= 5.
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As noted in section 3.2, for large values of α, the status distribution runs away to an end-
state in which a single individual possesses virtually all of the status in the society, and all other
individuals have virtually zero status. In this totalitarian end-state, the variance M
2
approaches
an upper plateau
c2¼ ðN1Þ�
S2¼N1;ð6Þ
where the average status is defined in the model to be �
S¼1, without loss of generality. The
upper plateau c
2
is the maximum possible value of M
2
(indicating the maximum level of
inequality of the society). For finite-sized systems, M
2
rises to this upper plateau at large times.
This large-time ascent to c
2
happens quickly for large values of α, and much more slowly for
smaller values of α(main plot of Fig 4a).
There therefore appear to be two relevant time-scales in the dynamics of the status distribu-
tions: one controlling the evolution away from the initial condition, and a second controlling
the long-time approach to the totalitarian end-state. To capture the dynamics of M
2
(t) for α>
0, we attempt to fit, to the simulation data, a sum of exponential functions:
M2¼c1ð1et=t1Þ þ ðc2c1Þð1et=t2Þ;ð7Þ
where τ
2
is a characteristic time controlling the rate of approach of M
2
to the upper plateau.
The first term in Eq 7 relates to the short-time dynamics of the status distribution, while the
second term relates to the long-time dynamics. Long-lived states are produced for values of the
model parameters αand δfor which τ
2
is much larger than the time, τ
obs
, over which the system
is observed (simulated), and τ
1
. When τ
1
tτ
2
,Eq 7 becomes M2ðtÞ  c1ð1et=t1Þ
Fig 3. Shape of status distributions as functions of α, with δ= 0.14. Increasing αleads to an increase in the level of
inequality of the society, while decreasing αleads to a decrease in the level of inequality. Inset: when α>0, M
2
does
not attain a plateau but continues to increase with t, at a rate that depends on α. The status distribution appears to be in
steady-state for small values of α>0 (e.g. α= 0.2 and α= 0.4 curves in the inset) when observed on short enough time
scales, while they are in fact not. For larger values of α>0 (e.g. α= 0.6 and α= 0.8 curves in the inset), the level of
inequality noticeably increases on the observation timescale, indicating a runaway of the status distribution toward an
end-state of maximum inequality. Distributions obtained after simulating up to time t¼64 ^t1.N= 10
5
,n
r
= 5.
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where, for α>0, c
1
represents an operational plateau value of M
2
(t) corresponding to the long-
lived state.
Fits of Eq 7 to simulated data are shown by the black dashed lines in Fig 4. For these fits, c
2
is fixed at its upper plateau value c
2
=N−1. Although the fits are imperfect (Fig 4c), they do
appear to capture the short-time “elbow” controlled by τ
1
and c
1
, and the long-time ascent
toward the upper plateau controlled by τ
2
(Fig 4b). Fig 4d shows the fit parameters as functions
of α: as αis increased, both c
1
and τ
1
increase, resulting in a slower evolution of M
2
(and there-
fore, of the shape of the status distribution) away from the initial condition. On the other
hand, as αincreases, τ
2
decreases, leading to a more rapid (long-time) approach to the upper
plateau. As αis increased to larger values, it becomes difficult to resolve the early-time
“elbow”, and thus difficult to obtain a meaningful fit of Eq 7. Furthermore, as shown in Fig 4c,
the shape of M
2
(t) appears to approach a straight line (at early times) as α!1, and then, to
bend upwards away from such an initial straight line when α>1.
For small α>0, the status distributions pass through three phenomenological stages, begin-
ning with an egalitarian initial condition and ending in the totalitarian end-state (see Section E
in S1 Appendix for details). The first stage pertains to the evolution of the status distribution
away from its initial condition over time scales of the order τ
1
and into a distribution with a
form similar to that of the α= 0 steady-state distribution. This “stage 2” distribution changes
only very slowly, eventually transitioning into a stage (“stage 3”) where high status individuals
are nearly guaranteed to win all fights. The duration of stage 2 decreases and essentially
Fig 4. Evolution of M
2
(t) for α>0. Values of αare indicated in the legend in (b), and δ= 0.2 for all curves. (a) shows
rapid ascent of M
2
(t) to upper plateau value for α= 1.0 and α= 2.5 (main plot), and the inset of (a) shows three of the
curves on log-log scale, with stages 1-3 of the evolution of M
2
(t) indicated for the α= 0.5 curve. (b) and (c) show the
main plot of (a) at different magnifications, to allow inspection of the fit of Eq 7 (dashed black lines) to the curves with
α0.6. (d) fit parameters, with y axis scale for t1
2shown on righthand side (parameters for α= 0 are from Eq 7,
assuming τ
2
=1). N= 100, n
r
= 500.
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disappears as αis increased, explaining the inability of Eq 7 to represent M
2
(t) for larger values
of α. In the asymptotic state of the evolution tτ
2
, all individuals have S0, except for a sin-
gle individual with status equal to the total status of the system.
Our findings are largely independent of the system size N. As shown explicitly in Section C
in S1 Appendix, the parameters controlling the early-time behaviour of M
2
(c
1
and τ
1
) remain
constant as the system size, N, is increased, whereas the parameters controlling the long-time
behaviour of M
2
(c
2
and τ
2
) both scale linearly with N. A proof that the time required to reach
the end-state, τ
end
, scales linearly with Nin the extreme scenario where δ= 1 and α=1is also
included in Section D in S1 Appendix, as a demonstration of the configurational reasons why
the long-time (approach to the end-state) evolution of the model dynamics increases in pro-
portion to the system size N.
3.4 Phenomenology of the characteristic time τ
2
when α>0
The characteristic time τ
2
controls the rate at which the system approaches the totalitarian
end-state when α>0. This characteristic time increases as αis decreased from large positive
values, as shown in Fig 4d. We can also see, from comparison of Eqs 3and 7, that τ
2
=1when
α= 0. We would like to know the functional relationship between τ
2
and the model parameters
in order to quantitatively characterize the transition between long-lived and runaway behav-
iours. To further explore this relationship, we consider an analogy with the barrier-like or
“activated” processes typical of many physical systems [47]. We find that the resulting Arrhe-
nius equation provides a good description of the relationship between τ
2
and the model
parameters αand δ. This allows us to determine, as a function of the model parameters, the
observation times over which status distributions can be considered long-lived, which we sum-
marize in the following section.
The rate of an activated process is proportional to an exponential term containing an
energy barrier scaled by temperature. The exponential term is multiplied by a pre-factor called
the attempt frequency, which is typically independent of temperature. This type of relationship
between rate and temperature can be found in many diverse physical phenomena, including
the rate of chemical reactions [47], the relationship between diffusion coefficients and temper-
ature [47], the rate of nucleation according to the classical nucleation theory [48], the viscosity
of strong glass-formers [49], and the blocking transition in superparamagnetism [50], as well
as in biology regarding, for example, the rate of chirping in crickets and of flashing in fireflies,
and in psychology, where human perception of time is related to body temperature through a
relationship of this form [51].
If the characteristic time τ
2
is regulated by αaccording to an activated process, then one
would expect the relationship between τ
2
and αto follow an Arrhenius equation of the form:
1
t2¼f0eab=a;ð8Þ
where α
b
is a term that plays a role similar to an energy barrier in an activated process and f
0
is
analogous to an attempt frequency. To test this idea, we plot the logarithm of N/τ
2
vs. α
−1
in
Fig 5a. The factor of Nis included due to the fact that τ
2
scales linearly with N, as discussed in
section 3.3 above and as shown in Section C in S1 Appendix.
The linear behaviour seen in Fig 5a confirms the relationship between τ
2
and αproposed in
the Arrhenius equation (Eq 8). In the figure, the δ-dependent slopes of the linear fits corre-
spond to −α
b
, and the intercepts to ln[Nf
0
]. The values of α
b
extracted from the linear fits in
Fig 5a are shown as a function of δin Fig 5b. As can be seen, α
b
diverges as δis decreased. The
red line in Fig 5b (main plot and upper inset) shows the function α
b
= 0.53δ
−1.21
.
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In Fig 5a, the y-intercepts of the linear fits appear to cluster around 0, suggesting that
the prefactor Nf
0
in the expression for N/τ
2
following from the Arrhenius equation is of the
order of 1 for the values of δconsidered. The y-intercepts do not, however, give a robust deter-
mination of the prefactor Nf
0
. This may be due to a change in functional form of Eq 7 as α
increases such that τ
1
vanishes. Alternatively, the prefactor Nf
0
can be directly determined by
setting α=1(equivalent to p= 1 in Eq 1) in the simulations and extracting τ
2
(α=1)
from M
2
(t). In so doing, we have assumed that tτ
1
and c
2
c
1
such that Eq 7 becomes
M2ðtÞ  c2ð1et=t2Þ.Nf
0
is then equal to N/τ
2
(α=1). This approach provides more robust
determinations of Nf
0
, which are shown in the lower inset of Fig 5b, along with a fit (red line)
of the function Nf
0
= 1.03δ
1.28
.
Substituting the expressions for Nf
0
and α
b
into the Arrhenius equation (Eq 8) gives:
N
t21:03d1:28e0:53=ðad1:21 Þ:ð9Þ
The Arrhenius equation and Eq 9 show that τ
2
diverges exponentially as α!0. This is
consistent with Eq 3, which indicates that τ
2
=1when α= 0. Moreover, the parameter δ
appears in both the pre-factor and the argument of the exponential of Eq 9, such that as δ!0,
τ
2
! 1, regardless of the value of α. This is consistent with the fact that for δ= 0 no status is
exchanged during a pairwise interaction and the initial status distribution is trivially stable.
These observations strongly suggest that stable status distributions only exist for α0 as well
as δ= 0. When α>0, Eq 9 allows us to identify the time scale of the transition between long-
lived societal structures and runaway toward the totalitarian end-state as we discuss in more
detail below.
Fig 5. Arrhenius relationship between τ
2
and αand δ.a) Plots of ln[N/τ
2
] vs. α
−1
for various values of δconfirm the relationship proposed in the Arrhenius equation
(Eq 8). The slope of each linear fit is −α
b
(δ). A discussion regarding the evaluation of errors on the extracted values of τ
2
is included in Section G in S1 Appendix. b)
Dependence of α
b
and Nf
0
on the parameter δ: the red line in the main plot (linear scale) and upper inset (logarithmic scale) corresponds to α
b
= 0.53δ
−1.21
; the red line
in the lower inset corresponds to Nf
0
= 1.03δ
1.28
.
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3.5 δ−αphase diagram
As shown in sections 3.2-3.4, for all positive values of δ, the model gives rise to three regions of
behaviour: true steady-state status distributions (α0), long-lived status distributions that
eventually arrive at the totalitarian end-state (small values of α>0), and runaway (rapid evolu-
tion) toward the totalitarian end-state (large values of α>0). These three regions and the tran-
sitions between are portrayed in a δ−αphase diagram in Fig 6.
Fig 6 is a summary of the main results of our model. In it, we see the three regions of behav-
iour described in sections 3.2-3.4. The region (marked with a roman numeral I) of infinite-
duration steady-state status distributions is separated from a region (II) of long-lived status
distributions by a transition that occurs due to an exponential divergence of the characteristic
time τ
2
as α!0
+
. Runaway behaviour (region III) occurs when a noticeable slope is observed
in the evolution of M
2
(t0 (see the inset of Fig 3 for α= 0.6 and α= 0.8). Whether such a slope
is observed or not depends on τ
2
(δ,α) and on the time, τ
obs
, over which the system is observed.
The location, in δ−αparameter-space, of the transition between regions II and III therefore
depends on τ
obs
, and can be determined directly from the data or via Eq 9, as described below.
Fig 6. δ−αphase diagram. The model exhibits three regions of behaviour in δ−αparameter-space: I (α0 or δ=
0): true (infinite-duration) steady-state status distributions; II (small values of α>0): long-lived status distributions; III
(large values of α>0): runaway behaviour. Within regions I and II, equi-M
2
lines (lines of equal standard deviation of
the status distribution) are shown with M
2
values indicated in parentheses below each equi-M
2
line. The location of the
transition between regions II and III is observation time-dependent, and is marked by the black triangular points for
τ
obs
= 10
4
and by the red triangular points for τ
obs
= 10
3
, as determined directly from the simulation data. The location
of the transition as determined by the Arrhenius relation between τ
2
and αis shown by the black (τ
obs
= 10
4
) and red
(τ
obs
= 10
3
) curves. System size N= 1000.
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We determined the location of the transition between regions II and III in two ways. First,
we used a simple criterion to equate the onset of runaway with the appearance of a positive
slope in the long-time portion of M
2
(t). In this way, the long-lived state corresponds to a pla-
teau value of M
2
(t) over a particular τ
obs
(where τ
obs
τ
1
). The long-lived state is considered
to be lost when, instead of a plateau, a positive slope is observed in M
2
(t) after a time τ
obs
has
transpired. The black and red triangular markers in Fig 6 indicate the onset of runaway as
determined by this criterion, for τ
obs
= 10, 000 and τ
obs
= 1, 000, respectively. Secondly, the
location of the transition can be determined using the Arrhenius relation presented in section
3.4 to determine the value of αfor which M
2
(t) increases by a sufficient amount after a time
τ
obs
has transpired. The location of the transition as determined by the Arrhenius relation is
shown by the curving curving black and red lines in Fig 6. Details about how these two
approaches were conducted are contained in Section I in S1 Appendix.
While αlargely determines the stability of the asymptotic status distributions, the exact
value of δ>0 influences the shape of the status distribution, as already seen for α= 0 in section
3.2. This is also true for the long-lived distributions. Essentially identical distributions can be
produced (within a particular simulation time) for different combinations of the parameters δ
and α(see Section H in S1 Appendix for details). Sets of such points are plotted as “equi-M
2
”
lines (lines of equal standard deviation of the status distribution) within regions I and II in
Fig 6.
3.6 Status distributions in the extended model
In section 2.1, we presented a simple extension to the original (two-parameter) model that
restricts which individuals can fight each other based on the proximity in their statuses. This
“extended model” introduces two new parameters: η, which sets a threshold Z�
S(where �
Sis the
average status of the system), such that individuals with statuses separated by an amount more
than this threshold are restricted from fighting with each other; and �, the probability with
which two individuals that have a separation of statuses greater than Z�
Sdo nevertheless fight
each other.
Fig 7 shows distributions of status generated by simulations of the extended model. A log-
linear scale is used in the righthand column of plots to allow inspection of the high-status tails
of the status distributions. In all subplots, α= 0 and δ= 0.2. When �= 1 (Fig 7a and 7b) the
original (two-parameter) model is recovered, such that the parameter ηhas no effect on the
shape of the status distribution. Also, when �= 1, the high-status tail of the distribution decays
exponentially, in accordance with the analytic result found by Ispolatov et al. [31] (Fig 7b).
When �is decreased, a “break” in the high-status tail of the distribution emerges, as can be
seen in the log-linear plot in Fig 7d. Following this break, the distribution enters a second
exponentially-decaying regime (black dashed line in Fig 7d) that ends with a cutoff. Increases
to ηcause the location of the break as well as the location of the peak of the distribution to shift
to higher values of S. The plateaus in M
2
(t) (insets in Fig 7) for � > 0 indicate that these distri-
butions are in steady-state.
When �= 0 (not shown), M
2
(t) does not obtain a plateau and continues to increase over the
duration of the simulation time. For this value of the parameter �, the system approaches an
end-state in which the majority of individuals have status approaching zero, and a small
minority of individuals have large statuses. In this end-state, the few high-status individuals
are prevented from interacting with each other because their statuses are separated by amounts
greater than Z�
S. The specific configuration of the �= 0 end-state depends on the particular
sequence of interactions. A positive value of �is therefore needed in order for the simulation
to obtain a unique steady-state.
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In the plots in Fig 7,α= 0. The distributions produced with α= 0, η>0, and � > 0 show a
plateau in M
2
(t). When α>0, M
2
(t) behaves qualitatively in the same way as the original (two-
parameter) model. That is, a long-lived state is observed for small values of α>0, and a run-
away for large values of α>0, where the location of the transition between the long-lived state
and runaway depends on the observation (simulation) time. Figs J1 to J4 in S1 Appendix show
how the extended model distributions evolve for representative values of α>0.
4 Comparison of model results with real-world data
In this section, we compare the results of our model to data from real-world social hierarchies
in two ways. First, we consider data on agonistic interactions (fights) from animal observation
studies, in section 4.1. We are able to make some general comments about the parameter val-
ues and the stage in the time evolution of the system for which the win and loss patterns in the
simulations resemble and are consistent with the animal behaviour data. We note that the
available data in this case is for small system sizes. Thus, the history of each particular pair of
individuals’ past interactions might be important since the individuals do recognize and
remember one another. This feature is not captured by our model, which is a better descrip-
tion for large groups of individuals, in which there is a large probability that an individual i’s
next interaction will be with an opponent with whom idid not interact recently.
Fig 7. Status distributions in the extended model. δ= 0.2 and α= 0 in all plots. Plots (b) and (d) show the
distributions on a logarithmic scale, in order to allow for inspection of the large-Stail. When �is decreased from 1, a
“break” in the distribution emerges, (particularly evident on the logarithmic scale) corresponding to a society with
distinguishable low status and high status groups or classes. The black dashed lines in (b) and (d) are maximum
likelihood fits of exponential distributions with lower-bound at S= 2.25 in (b) and [lower-bound, upper-bound] at S=
[0.75, 5.0] in (d), where plotted fit line in (d) is extrapolated beyond S= 5.0. System size N= 10
5
.
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Unfortunately, there are currently no observational interaction data for sufficiently large
groups of individuals. In order to compare the distributions of societal status from the model
with real-world social hierarchies, we therefore seek a measurable quantity that serves as a
proxy for societal status in large social groups. Such a proxy must allow the assignment of a sta-
tus value to all individuals in a large society. We have reviewed potential proxies for status in
non-human animals, and found that body-size in insects seems to be the only such quantity
for which data is currently available for large groups. We present a comparison to our model
in section 4.2.1. In the case of humans, socioeconomic data about large groups is available and
we justify the use of household income as a proxy for societal status in large human societies
and compare this proxy to status distributions from our model in section 4.2.2.
4.1 Agonistic interactions in small groups of animals
In many studies across a wide range of taxa [52], researchers of animal social behaviour have
observed and recorded agonistic interactions between pairs of individuals. These interactions,
which include aggressions, physical and non-physical threats, and submissive behaviours [1]
can be considered as “fights” in which a winner and loser can be identified. From these obser-
vations, an “interaction matrix” can be created, with a row and column for each individual,
and where each entry (i,j) of the matrix represents the number of times ihas defeated jin a
pairwise fight. Various methods exist to determine a rank-ordering of the individuals in the
society from the data contained in the interaction matrix [26,53–55]. Correlations can then be
investigated between hierarchical rank and outcomes such as health, reproductive success,
quality of social relationships, and preferential access to resources of the individuals in the
social group.
A common observation in many such studies is that the higher-ranked individual in a pair
wins the large majority of the fights against the lower-ranked individual. However, in most
datasets, a small number of interactions occur in which the lower-ranked individual wins the
fight (called “reversals” in the animal behaviour literature). In our model, the distributions of
status that most closely resemble this scenario are highly-skewed distributions in which p1
(see Eq 1) for many pairs of individuals, such that reversals are rare but still possible. This
excludes α0, since reversals are frequent in this range of parameter space, and highlights the
relevance of the long-lived states observed in our model (see Fig 6). For small values of α>0,
the said distributions occur at late stages in the evolution of the system, and for larger values of
α>0, at the said distributions occur at the stage immediately following the evolution of the
system away from the initial condition (see the discussion regarding the three phenomenologi-
cal stages of the evolution of the system described in section 3.3 above and in Section E in S1
Appendix).
In Fig 8, we compare interaction matrices from an animal observational study with “simu-
lated interaction matrices” from our model. We use the largest interaction matrices included
in the recent review of Shizuka and McDonald [52], which were from a study of adult female
mountain goats by Co
ˆte
´[56]. Co
ˆte
´published interaction matrices recorded over four sum-
mers, from 1994-1997, where N= 26 individuals were present in all four years. We ran simula-
tions of the model for a system size N= 26, in which we recorded interaction matrices for the
individuals in the simulation. These “simulated interaction matrices” were recorded over four
separate time periods, where the duration of each time period was equal to the number of
interactions recorded during each of Co
ˆte
´’s summers of observation (Co
ˆte
´recorded an average
of 279 interactions/summer for the 26 goats, using ad libitum sampling). The simulated inter-
action matrices were each separated by a time period corresponding roughly to the number of
pair-wise interactions that N= 26 female mountain goats are expected to have in one year,
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estimated here to be approximately 10
5
interactions from the rate of 3 interactions/individual/
hour found in Ref. [57]. We allowed each simulation to evolve to a time t0^t1before record-
ing the first simulated interaction matrix, to bypass the initial transient evolution of the simu-
lation away from the egalitarian initial condition. The mountain goat interaction matrices
change very little from year to year. This corresponds to a very slowly evolving simulation,
where the status distribution remains essentially unchanged between recordings of the
Fig 8. Comparison of model results with animal interaction data. (a) David’s Scores, D, calculated from the
mountain goat interaction data of Ref. [56], for each of four summers from 1994-1997 (solid points). Individuals are
ordered by decreasing Dalong the x-axis, and the value of Dis plotted on the y-axis. The coloured bands show 5%–
95% ranges for Dcalculated from interaction matrices obtained from simulations of the original (two-parameter)
model with δ= 0.01, α= 0.99, and t0
0¼5105interactions, where t0
0¼N t0=2as per the definition of time in section
3.1. n
r
= 100 realizations of the simulation were performed. (b) Histogram of p
>2
, the probability that the more
successful individual won in a pairwise interaction, considering only those pairs of individuals that engaged in three or
more interactions. p
>2
was calculated from the interaction matrices corresponding to the blue points (animal data
from 1994) and blue band (simulation) in panel (a), and for a simulation with α= 0 (black). For the simulations, the
height of each histogram bar shows the average and the error bars show the standard deviation of the number of
counts per bin. Inset of (b): M
2
(t) for the simulations from which the coloured bands in (a) and filled coloured
histogram bars in (b) were obtained, showing (dashed coloured lines) the periods in the simulation during which
interaction matrices were recorded. Panels (c) and (d): heatmaps showing individual i’s probability of defeating
individual jin a pairwise interaction, calculated (c) from the 1994 mountain goat data, and (d) from the simulations
from which the blue band in (a) was obtained (averaged over the n
r
= 100 realizations). Grey squares indicate that no
interaction occurred between iand j. Probability values indicated in the colour-bar in the top-right corner of (d) are
applicable to both (c) and (d).
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simulated interaction matrices, highlighting again the relevance of the long-lived states
observed in our model. This occurs for small values of the parameter δ(δ= 0.01 was used in
Fig 8).
In Fig 8a, we show a comparison of the “David’s Score”, D, calculated from the simulated
and real interaction matrices. Dis a commonly-used score that allows a ranking of the individ-
uals in an interaction matrix. It is defined as follows [53,58]. Let s
ij
be the number of times
individual ihas won in an fight against individual j, and let n
ij
be the total number of fights
between iand j.P
ij
=s
ij
/n
ij
is then the proportion of wins that ihas experienced in fights with j.
The proportion of losses that ihas experienced in fights with jis 1 −P
ij
=P
ji
, and when n
ij
= 0,
P
ij
and P
ji
are set equal to 0 [53]. Let wi¼PN
j¼1;j6¼iPij and wi;2¼PN
j¼1;j6¼iwjPij, such that the
sum in w
i,2
is weighted by the w
j
of each opponent j. Similarly, let li¼PN
j¼1;j6¼iPji and
li;2¼PN
j¼1;j6¼iljPji. The David’s Score of an individual is D
i
=w
i
+w
i,2
−l
i
−l
i,2
.Dthus depends
not only on the proportions of wins and losses experienced by an individual, but also on the
win and loss proportions of those with whom the individual has fought. For example, if an
individual idefeats an opponent who has won a large proportion of his or her fights, this
causes a large increase D
i
, whereas if iloses to an individual who has lost a large proportion of
his or her fights, this causes a large decrease in D
i
.
Fig 8a shows that the set of D’s calculated from the simulated interaction matrices resembles
the one from the mountain goat interaction matrices, and that this resemblance is maintained
after allowing a large number of interactions (10
5
) to take place between recording the simu-
lated interaction matrices. Fig 8b shows that, in the mountain goats, the more successful indi-
vidual wins the fight almost all of the time, considering those pairs of individuals that engaged
in three or more fights. A similar result was obtained from the simulation for the parameter
values used in Fig 8a.Fig 8b also shows that, when α= 0 (such that p= 0.5 in Eq 1), there are
few pairs for which the more successful individual wins all fights (as expected), such that α>0
is required in order to have a good comparison with the animal data. Only those pairs of indi-
viduals that had engaged in three or more fights were considered in the histograms in Fig 8b,
in order to avoid high fluctuations due to small numbers of fights. Also included in Fig 8 are
heatmap plots showing the probability that individual idefeats individual jin a pairwise fight,
calculated from the mountain goat interaction matrices (Fig 8c) and the simulated interaction
matrices (Fig 8d), showing visually that reversals are rare but non-negligible.
Many animal observation studies have found similar interaction matrices to Co
ˆte
´’s moun-
tain goat matrices, in that they have small numbers of reversals [52]. The results from our
model therefore suggest that many animal groups have highly-skewed distributions of status
and relatively large values of the authoritarianism, α. On the other hand, it is known that ani-
mal species with more complex social organization can have a larger number of reversals and
intransitive relationships (where individual iwins the majority of fights against j, who wins the
majority of fights against k, who in turn wins the majority of fights against i) [3,59]. This may
correspond to a less highly-skewed distribution of status in our model. Little is presently
known about the frequency of reversals and intransitive relationships in large social groups
due to a lack of interaction data for large groups. A key problem in this regard is that the pro-
portion of pairs of individuals for which no interactions are observed generally grows with sys-
tem size in interaction matrices from observational studies, due to the increasing difficulty of
observing all pairs [52].
Due to the small value of δrequired to obtain quasi-stationarity of the status distribution
over large numbers of fights in the simulations in Fig 8, the model suggests that relatively small
amounts of status (e.g., one one-hundredth of an individual’s current status when δ= 0.01) are
exchanged in a single interaction. In female mountain goats, rank is strongly correlated with
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age, and older goats almost always win interactions against younger goats [56]. Larger values
of δand smaller values of αmay apply in other animal societies in which ranks change more
frequently or where age is a less important factor, including in some primate species in which
complex power struggles leading to takeovers are commonly observed [60,61].
We also note that for the N= 26 individuals present in all 4 years of Co
ˆte
´’s study, there was
no tendency for individuals to interact more frequently with those close in rank than with
those far away in rank, although such a tendency is observed when considering all individuals
in a single summer, as shown in Fig 6 of Ref. [56] (see Section A in S2 Appendix for further
details). Our subset of N= 26 individuals leaves out those individuals who enter the group (pri-
marily, by ageing to the age of maturity of 3 years old) and who leave the group (primarily, by
dying at old age) from one year to the next, which indicates that the tendency observed by
Co
ˆte
´for individuals to interact more frequently with those closer in rank is mostly due to
interactions involving the oldest and youngest individuals within the group. The absence of
this tendency in the N= 26 subgroup that we consider supports our use of the original (two-
parameter) model in the simulations in Fig 8.
Lastly, for the model simulations shown in Fig 8, the characteristic time τ
2
59 years, sig-
nificantly longer than the 12-15 year life expectancy of mountain goats [62]. The ultimate col-
lapse of the system to the totalitarian end-state exhibited by our model may therefore not be
relevant for mountain goats, since factors such as births, deaths, maturation of juveniles, and
immigration, which are not considered in our model but which occur on time-scales signifi-
cantly shorter than τ
2
, will change the long-term dynamics of the system significantly.
4.2 Proxies for status in large social groups
In the comparison of our model with animal interaction data shown in section 4.1, we
recorded the simulated interaction matrices starting from an arbitrary initial distribution of
status, S(t
0
). In principle it is possible, for an arbitrary value of α, to estimate the {S
i
} values of
the animals in an observed interaction matrix. This would require enough interaction data to
estimate the win probabilities for a sufficient number of pairs of individuals such that the ratio
S
i
/S
j
could be calculated from Eq 1 for these pairs, and then all {S
i
} calculated from these ratios.
In so doing, one of the {S
i
} can be set to an arbitrary value since the dynamics of the model are
independent of the value of the (conserved) average status, �
S, of the system. The data in pub-
lished animal interaction matrices (including in Ref. [56]) is insufficient for this purpose,
because there are few pairs of individuals where the win probability p<1, making it impossi-
ble to obtain a meaningful estimate of {S
i
}. Given the difficulty of determining statuses of
individuals in the model directly from observed interactions in animal behaviour studies, to
compare our model results with real-world societies we seek a measurable quantity that can be
used as a proxy for societal status in (large) social groups.
4.2.1 Intraspecific body size distributions in social insects. We have reviewed a number
of measurable quantities that may serve as proxies for societal status in non-human animal
groups and these are presented in Table B in S2 Appendix. The only such quantity for which
we were able to find data that would allow one to assign a status value to all individuals in a
large group (N100) is body size in social insects. Data collected and reviewed by Gouws and
co-workers shows that body size distributions in social insects are typically right-skewed as
opposed to being normally distributed in non-social insects [63]. The distributions of status
formed in our model are also right-skewed (e.g. see Fig 2), and thus compare favourably with
the body size distributions of social insects. While a more detailed comparison between simu-
lated status distributions and intraspecific body size distributions in social insects would be
desirable, it is currently problematic because published data either does not involve individuals
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from a single colony or does not contain enough information about the distribution to allow a
comparison (specifically, both the variance and skewness of the proxy distribution are at least
required).
While we were unable to find other proxy data for societal status in large groups of non-
human animals, such data does exist for large groups of humans. We thus focus the remainder
of this section on a comparison of the distributions of status produced by our model to a proxy
for societal status (household income) in humans.
4.2.2 Income distributions in humans. In humans, researchers use a theoretical con-
struct called socioeconomic status (SES) to assign positions to individuals in the social hierar-
chy. Measurable characteristics such as income, education, occupation, and wealth are used as
single-factor indicators of SES, or are combined in various ways to obtain composite indicators
of SES [64]. When income data is available, it is considered to be a critical component in deter-
mining SES [65] and, when used as a single-factor indicator of SES, has been found to have
greater explanatory power (for example, of the relationship between SES and mortality) than
education or occupation [66]. Here, we use household income in Canada and the USA as a
proxy for societal status, because income data is readily available in online datasets for these
two countries (unlike data on wealth) and it is quantitative (unlike level of education or occu-
pation), making it straightforward to analyze and compare to our model results. We use house-
hold income, rather than personal income, as our proxy for societal status, in order to avoid
artifacts related to income sharing within a nuclear family unit, such as when a spouse decides
not to work or to work below his or her maximum market value [67]. We thus follow Ref. [66]
and many other studies (e.g. [68–70]) and use household income as an established proxy for
socioeconomic status.
Fig 9 shows the (pre-tax) distribution of Canadian household incomes from the 2001 Cana-
dian census [71], as well as a model-generated status distribution for the two-parameter ver-
sion of our model (section 2). The simulated system contained N= 312, 513 individuals, which
is the number of households in the public-use census sample. The average status of each indi-
vidual was set equal to the average household income in the data, such that �
S¼55;536. The
model parameter αwas set equal to 0, in order to produce true steady-state status distributions,
for convenience in obtaining a fit to the real data that is independent of observation time, τ
obs
.
The parameter δwas then adjusted until a good fit was obtained with the income data (δ=
0.35 for the plot in Fig 9). We note that essentially the same status distributions—albeit not
steady-state but only long-lived—can be obtained for (δ,α) values that trace out an equi-M
2
line in δ−αparameter-space (see Fig 6 above and Section H in S1 Appendix).
The original (two-parameter) version of our model is sufficient to fit the Canadian house-
hold income distribution. This suggests that our very simple model may capture some essential
features of the interactions that give rise to social hierarchy in large groups of individuals. The
two model parameters, δand α, which determine the outcomes of pairwise interactions, may
be interpreted as societal features since their values are held constant for all interactions that
occur within the society. Particular societies or species may have different values of one or
both of these parameters, leading to different societal structures represented by the distribution
of status. Additional comments about the potential real-world implications of the time evolu-
tion behaviour of the model are included in section 5.
A shortcoming of the Canadian data is that upper income cutoffs were applied to the pub-
lic-use dataset by Statistics Canada, for the purpose of protecting confidentiality of wealthy
individuals. This results in poor data quality at the upper income end of the Canadian house-
hold income distribution. However, there is evidence that income distributions from many
countries have a low-to-middle-income part that is well described by a distribution function
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containing an exponential decay, and a separate, high-income part that decays more slowly
[30,73]. The inset of Fig 9 shows the Canadian household income distribution and the model-
generated status distribution on a log-linear scale, in order to allow inspection of the large-S
tail. The expected slower-than-exponential decay of the upper-tail is not seen in this data. This
motivates us to examine the USA household income distribution, in which the distinct low-to-
middle and upper income parts of the distribution are present.
Fig 10a shows United States household income distributions for the years 1990, 2000, and
2015 [74]. The USA datasets are 1-in-100 national random samples of the population. Dollar
values for each of the three datasets have been adjusted to 1999 values in order to permit a
comparison across time. In these distributions, the presence of an approximately exponen-
tially-decaying low-to-middle-income part (initial straight-line decrease in the inset of Fig 10a
beginning after the peak at S0.25 ×10
5
and ending at S0.25 ×10
6
), separated from a
more slowly-decaying high-income tail is visible. The “break” point between the lower and
upper parts of the data is present for all three curves and can be seen in the inset of Fig 10a at
S0.25 ×10
6
. This break is observed in the income distributions of many different countries
[30,73].
Fig 9. Fit of original (two-parameter) model status distribution to Canadian household income distribution.
Simulated distribution (red curve, δ= 0.35 and α= 0), compared to the distribution of Canadian household incomes
from the year 2000 (blue curve). Scaled variance, M2=�
S¼0:536 and skewness, γ= 1.3 for the simulated distribution,
and M2=�
S¼0:534 and γ= 1.4 for the proxy distribution. The x-axis of the main plot has been cut at S= 2.5 ×10
5
. The
large peak in the data at $12, 000 (S= 0.12 ×10
5
) is most likely due to welfare benefits provided by the government, and
the peak at $12, 000 (S= 1.2 ×10
5
) comes from an upper income cutoff applied to the public-use data by Statistics
Canada [72].
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Fig 10b shows the 2015 USA household income distribution and a simulated distribution
from the extended model. The main plot (linear scale on the y-axis) shows that the simulated
distribution fits well to the low-to-middle income part of the distribution. The inset (logarith-
mic scale on the y-axis) shows that the simulated distribution also contains a break between
the low-to-middle and high-income parts of the distribution, mirroring the break in the data.
The value of the parameter ηwas chosen such that Z�
S¼SB, where S
B
is the location of the
“break” point in the data, estimated from the data to be at $275, 000. Changes to the value of �
result in a poorer fit (see Figs C1 and C2 in S2 Appendix). As expected, �is small, such that
only 8% (�= 0.08) of the pairwise interactions that would not occur according to the threshold
criterion do occur. Two alternative ways to restrict the pairwise interactions between compet-
ing individuals that lead to quantitatively similar behaviour are described in Section E in S2
Appendix; for example, a simple, additional extension to the model that allows all individuals
with statuses greater than Z�
Sto interact with each other shows an improved fit to the proxy
data.
Several econophysics studies have found power-law distributions in the high-income tail of
personal income distributions [30,73]. However, a graphical analysis (Fig 11) comparing best
fits of exponential and power-law distributions to the high-income tails of the 2000 and 2015
USA household income distributions shows that the household income data that we use as a
proxy for societal status is not consistent with a power-law distribution but is consistent
with an exponential distribution over large ranges. This is also confirmed by Kolmogorov-
Smirnov tests for both distribution types. Further details are provided in Section D in S2
Appendix [76–79].
Fig 10. USA household income distributions and fit of extended model. (a) USA household income distributions for three different years (indicated in legend).
Dollar values have been converted to 2015 values to allow comparison of the different datasets [75]. A “break” in the data separating the high-income tail from the low-
to-middle part of the income distribution can be seen in the inset at S$250, 000 (S= 0.25 ×10
6
). Upper-income cutoffs have been applied to the American data by
the governmental agency that provided the data, causing the plateau in ln[p(S)] (inset) for the highest income values. (b) Fit of extended model status distribution to
USA household income distribution. Simulated distribution (red curve) with parameters δ= 0.4, α= 0, η= 3.5, �= 0.08 compared to the distribution of USA
household incomes from the year 2015 (blue curve).
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The distinct low-to-middle and upper status parts of the data show the presence of two dis-
tinct groups or classes within the society. In the model, these two distinct groups emerge when
the frequency of interactions between individuals with large differences in status is reduced.
The model thus suggests that societal conditions that limit interactions between individuals
with large differences in societal status may produce or maintain distinct social classes. Indi-
viduals belonging to the upper status class may have a self-interest in reinforcing such societal
conditions in order to preserve their positions in society. Societies with policies that promote
interactions between individuals with large differences in societal status may have less distinct
class structures.
5 Discussion
We have presented a simple winner-loser model of the formation and evolution of social hier-
archy, based solely on interactions (fights) between individuals that result in the transfer of
societal status from the loser of the fight to the winner. We showed that the model exhibits
regions in parameter-space in which the asymptotic distributions of status produced by the
model either show a continuous unimodal behavior or take on a degenerate form, in which a
single individual possesses all of the society’s status. In the latter case, intermediary distribu-
tions are long-lived for small positive values of the model parameters. Here, “long-lived” refers
to quasi-stationarity of the status distribution, where, over a sufficiently short observation
time, the status distribution remains essentially unchanged. This is quantified through the
characteristic time τ
2
, which controls the evolution of the system toward the end-state.
Our model thus suggests that there are two fundamental characteristics of status-determin-
ing interactions in a society—the level of intensity of interactions (δ) and the degree of authori-
tarianism (α)—that determine both the outcomes of the interactions and whether the society’s
structure will be stable or preserved for long times before undergoing eventual deterioration.
Fig 11. Functional form of high-income tail of USA household income distribution. Power-law (dashed black line) and exponential (solid red line) distributions
with lower and upper bounds on the fitted distribution chosen to correspondto the full high-income tail.Srepresents USA household income data in 1999 USD. The
curves for 2015 have been shifted down in the plots for better visualization.
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These two parameters together with optional parameters restricting the interactions between
individuals control the shape of the (intermediary) status distribution, which becomes more
unequal (larger variance) as either αor δis increased.
In comparing the status distributions produced by simulations of the original (two-parame-
ter) and extended models with the proxies for societal status in section 4.2, the parameter α
was set equal to 0. However, as shown in Section H in S1 Appendix, essentially the same long-
lived status distributions can be obtained for (δ,α) values that trace out an equi-M
2
line in δ−
αparameter-space. Starting with a given value of δand α= 0, one can locate an equi-M
2
line in
Fig 6. Then, following the equi-M
2
line in the direction of increasing α, one eventually arrives
at the transition between long-lived states and runaway (where the location of the transition
depends on the time over which the society is observed). If the value of δof a society can be
determined by fitting the model (with α= 0) to a proxy for the society’s status distribution,
then the corresponding equi-M
2
line may provide an indication of the maximum level of
authoritarianism for which essentially the same status distribution can be maintained over a
specific time interval. This maximum level of authoritarianism would be reached when the
equi-M
2
line intersects with the boundary separating regions II and III for this specific time
interval, see Fig 6 for examples.
Similarly, the presence of a characteristic time scale controlling the longevity of the inter-
mediary distributions suggests a limit on the extent to which societal inequality can increase
(e.g., due to societal changes that cause an increase of one or more of the parameters) before a
runaway deterioration occurs. Whether a real society in fact approaches the end-state might
depend on how this characteristic time scale compares with other time scales neglected in our
model, such as the rate at which the society experiences external perturbations including wars
with other societies or major environmental changes, and internal perturbations related to the
effects of birth, death, immigration, and aging of individuals. For example, in the comparison
of the model with agonistic interactions in animals shown in section 4.1, τ
2
59 years, signifi-
cantly longer than the 12-15 year life expectancy of mountain goats [62], such that births,
deaths, maturation of juveniles, and immigration may change the long-term dynamics of the
system in such a way that it remains far from the end-state predicted by our model.
Extending our original (two-parameter) model by introducing an additional model rule
that adjusts the probability with which individuals interact with one another based on the dif-
ferences in their statuses, we can produce stable and long-lived status distributions which have
identifiable low-to-middle and upper status regions. The status distributions in our extended
model show good agreement with the distribution of household incomes in the USA (see Fig
10b), which we use as a proxy for societal status in large social groups. This appears to be the
first model in which the two-part structure of the proxy distribution emerges by self-organiza-
tion based solely on interacting individuals, without requiring any exogenous influence such
as redistribution through taxation. Several analyses of personal income distributions have
found that the low-to-middle-income part of the distribution decays exponentially and that
the high-income tail decays like a power-law [30,73]. Based on these analyses, various econo-
physics models have been propopsed with the goal of generating distributions of income with
two-part shapes in which the lower-to-middle part of the income distribution follows an expo-
nential distribution, and the upper tail follows a power-law distribution [39,80,81]. Among
the models that generate a distribution of income or wealth as a self-organizing process based
on interactions between individuals, several are able to produce either a power-law decay in
the upper tail, or an exponential decay in the lower part of the distribution, but not both. The
status distributions produced by the original (two-parameter) version of our model do not
have two-part structures, and therefore never contain a “break” in the large-Stail similar to
that seen in the insets of Fig 10. But the extended version of our model can produce two-part
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structures, where both the regime leading up to and the regime following the “break” decay
exponentially. Our model thus suggests that societal structures containing distinct social clas-
ses arise when interactions between individuals with large differences in social status are lim-
ited or restricted, as occurs, for example, in residential segregation in the USA [45,82,83].
A necessary foundation for more advanced studies of social hierarchy is the exploration of
the simplest possible realistic models, including the determination of their limits. This was
the goal of the present article. Future, network-oriented models may incorporate features such
as the histories of interactions between individuals [84–86] and cooperative behaviours includ-
ing the formation of coalitions [43,87–89] and mobbing [90,91]. Such models may provide
deeper understanding about the origins and evolution of societal structures, including the
mechanisms responsible for societal destabilization or collapse.
Supporting information
S1 Appendix. Supporting information for section 3.
(PDF)
S2 Appendix. Supporting information for section 4.
(PDF)
Acknowledgments
The authors thank Jordi Baro
´for assistance on the statistical analysis of the USA household
income distributions in section 4.
Author Contributions
Conceptualization: Joseph Hickey, Jo¨rn Davidsen.
Data curation: Joseph Hickey.
Formal analysis: Joseph Hickey.
Funding acquisition: Joseph Hickey, Jo¨rn Davidsen.
Investigation: Joseph Hickey.
Methodology: Joseph Hickey, Jo¨rn Davidsen.
Project administration: Joseph Hickey, Jo¨rn Davidsen.
Resources: Jo¨rn Davidsen.
Software: Joseph Hickey.
Supervision: Jo¨rn Davidsen.
Validation: Joseph Hickey, Jo¨rn Davidsen.
Visualization: Joseph Hickey.
Writing – original draft: Joseph Hickey.
Writing – review & editing: Joseph Hickey, Jo¨rn Davidsen.
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