Social Complex Systems as Multiscale Phenomena: From the Genome

to Animal Societies

Ilvanna Salas and Sebastián Abades

GEMA Center for Genomics, Ecology & Environment, Facultad de Estudios Interdisciplinarios, Universidad Mayor,

Santiago 8580745, Chile

Keywords: Social Complexity, Genome Complexity, Genome Architecture, Complexity Trade-off, Sociogenomics,

Comparative Genomics.

Abstract: For decades, researchers have studied animal social phenomena and aimed to answer: What is social

complexity? Are some animals more socially complex than others? However, social complexity concepts are

far from agreed and the field is still open to new research approaches. In this position paper, we propose to

frame social complexity as a problem of organized complexity (whereby multiple scales and interactions

across components produce patterns and organization). To improve our understanding of sociality, we

encourage building a “social complexity theory” at the intersection of complex systems, behavioral ecology,

and social systems concepts. This manuscript highlights the importance of considering social complexity as

a multiscale phenomenon and raise the presence of trade-offs between scales. We illustrate the relationship

between complexity and scales with examples from genomic to population scale in animal societies. Moreover,

we suggest giving special attention to genome-scale studies to provide a common ground for comparing

complexity among animal species and put forward comparative genomics as an approximation to drive the

understanding of the evolution of social complexity.

1 INTRODUCTION

In 1947, Warren Weaver proposed to divide scientific

problems into three levels of complexity depending

on how variables are treated. The first level named

“the two-variable problems of simplicity” deals with

pairwise relationships and is exemplified by the

physical science before 1900. The second level is

represented by “problems of disorganized

complexity”, wherein methods can deal with billions

of variables using probability theory and statistical

mechanics (e.g., the motion of the stars which form

the universe, or the fundamental laws of heredity)

(Weaver, 1948). These approaches are two extremes

in a complexity range that leaves the impression that

scientific inquiry has concentrated its efforts in an

extremely incomplete two-variable description of the

world or, in the opposite extreme focusing on dealing

with an astronomical number of variables. Despite the

number of variables, the outcome seeks the same: to

provide a simplified view of the world that overlooks

the diversity of complex phenomena.

According to Weaver (1948), the middle area

of the range has been devoid of attention. The main

feature of this middle region does not regard the

number of variables but on its interactions and their

tendency to produce patterns and organization. Here

lives Weaver’s third level of complexity problems,

called “problems of organized complexity”, which

deal simultaneously with a sizeable number of

variables interconnected into an organic whole.

Waver suggests that these problems cannot be

handled with statistical techniques aimed at

simplicity; instead, science must embrace these

problems of organized complexity differently if

intended to answer questions such as: Is a virus a

living organism? How do genes organize to express

all the features of an individual? Do molecules “know

how” to replicate their pattern?

In this position paper, we elaborate on some

ideas important to the study of animal sociality from

the perspective of complex systems. Here we propose

that social complexity is a problem of organized

complexity and raise social complexity as a

multiscale phenomenon. In our view, this approach

may get us closer to address questions such as: What

is social complexity? Can social complexity be

measured? Are some animals more socially complex

than others? And if this is the case, why?

100

Salas, I. and Abades, S.

Social Complex Systems as Multiscale Phenomena: From the Genome to Animal Societies.

DOI: 10.5220/0010492801000106

In Proceedings of the 6th International Conference on Complexity, Future Information Systems and Risk (COMPLEXIS 2021), pages 100-106

ISBN: 978-989-758-505-0

2 A SUMMARY OF THE STUDY

OF SOCIAL COMPLEXITY

For decades, researchers have tried to establish links

between evolution, ecology, sociality, and cognition

to explain why some species evolved to form

complex societies. However, social complexity is still

poorly understood (Hobson et al., 2019). The

complexity of social systems seems to vary across

species. Animal societies vary in size, composition,

number of social units, reproductive skew, parental

care, cooperation, and competition. This diversity is

the outcome of multiple social solutions that could

have originated from different evolutionary paths to

fit in a wide range of niches. (Hobson et al., 2019;

Kappeler, 2019; Kappeler et al., 2019).

According to Freeberg et al. (2012), complex

social systems can be defined as “those in which

individuals frequently interact in many different

contexts with many different individuals, and often

repeatedly interact with many of the same individuals

over time”. This definition emphasizes interactions

among agents as the core aspect underlying socially

complex instances. Therefore, it is a fertile conceptual

substrate to explore alternative definitions based on

complex system concepts.

Different authors have provided multiple

definitions of social complexity, some examples are:

1) More complex societies are those with many

individuals; 2) More complex societies are those

where groups have social roles, such as members of

morphologically different castes; 3) Complex

societies are those with multiple levels of social

groups; 4) Complex societies are those where social

relationships between group members can be

individually differentiated (Bergman and Beehner,

2015; McShea and Brandon, 2010; Freeberg et al.,

2012; Kappeler et al., 2019; Rubenstein and Abbot,

2017).

In line with multiple definitions, multiple

approaches have been used to estimate social

complexity, mostly based on taxonomic dependent

traits, making comparative studies hard to implement

or even unviable. Some recent attempts have been

made to unify concepts and make social complexity

studies more accurate. Kappeler et al. (2019)

proposed a framework for the systematic study of

social complexity based on four components: social

organization, social structure, mating system, and

care system. Despite this framework provides a

comprehensive set of recognizable features useful to

characterize and compare social species, it does not

deepen on how to conceptualize and quantify

complexity in this context (Hobson et al., 2019).

A different proposal was made by Holland and

Bloch (2020), who argues that “we need to switch the

measure of complexity in individual social traits from

semantic discussions to quantitative social traits that

can be correlated with molecular, developmental, and

physiological processes within and across lineages of

social animals”. To achieve this goal, they suggest

combining key social complex traits into

multidimensional lineage-specific quantitative

indices, thus enabling comparisons across species.

However, Hobson et al. (2019) point out that,

although multidimensional approaches may improve

comparisons of social systems, combining these

measures is unlikely to provide additional

information on social complexity.

Empirical studies on social complexity are

becoming common. Therefore, it is important to

notice that an appropriate conceptualization of social

complexity is critical before “jumping into

quantifying it”. The main behavioral characteristics

of any complex system are emergence, adaptability,

and dynamism. But currently, social complexity

studies based on single traits fail to account for

system-wide organizing properties and limit the

understanding of the social system as a whole,

resulting in a mischaracterization of large-scale

behavior (Aziza et al., 2016; Hobson et al., 2019;

Siegenfeld & Bar-Yam, 2020).

Besides theoretical problems, according to

Kappeler (2019), questions concerning distribution

and determinants of social complexity represent

important open questions for future research.

Therefore, efforts to improve understanding of social

complexity are needed; and comparative studies can

advance understanding of which traits and

mechanisms influence, or are influenced by, the

evolution of social complexity (Holland and Bloch,

2020).

Hobson et al. (2019) affirm that one way to

evaluate and compare the level of complexity of

animal societies is to incorporate some of the

fundamental concepts of complex systems

theory(Hobson et al., 2019)(Hobson et al., 2019).

These authors highlight three concepts that apply to

animal social systems: (1) Scales of organization:

scales, levels, and perspectives that constitute

complex systems, from which they can be described;

(2) Compression: an information-reduction process

that summarizes or abstracts patterns in observations

and (3) Emergence: local interactions between

components give rise macro-level order phenomena,

like those documented in animal movement and

problem-solving studies. These concepts are not

intended to be direct measures of social complexity,

Social Complex Systems as Multiscale Phenomena: From the Genome to Animal Societies

101

but rather should be used as a guide when using or

developing approaches to social complexity.

A practical way to study social complexity is with

computational simulations, defined as “the imitation

overtime of the operation of a real-world system or

process”. Systemic approach simulations consider the

system as a whole and focus on the dynamic

relationships between its components, allowing to do

virtual experiments to test different scenarios and

make predictions of the behavior of a system (Aziza

et al., 2016)

When talking about social complexity, it is

important to keep in mind that the description of a

system depends on the level of detail used to describe

it. To fully characterize a system it is necessary to

understand it across multiple scales (Siegenfeld and

Bar-Yam, 2020). Therefore, we consider it is

important to recognize social complexity as a

multiscale phenomenon and that this notion is useful

to reconcile the diversity of definitions that abound in

the literature.

3 SOCIAL COMPLEXITY AS A

MULTISCALE PHENOMENA

“As far as I am able to judge, the conditions of life

appear to act in two ways --directly on the whole

organization or on certain parts alone and indirectly

by affecting the reproductive system. With respect to

the direct action, there are two factors: namely, the

nature of the organism and the nature of the

conditions… The former seems to be much the more

important; for nearly similar variations sometimes

arise under, as far as we can judge, dissimilar

conditions; and, on the other hand, dissimilar

variations arise under conditions which appear to be

nearly uniform. There can, however, be little doubt

about many slight changes, such as size from the

amount of food, colour from the nature of the food,

thickness of the skin and hair from climate, etc.”

(Darwin, 1859).

In this quote from The Origin of the Species,

Darwin mentions two factors that can directly affect

organisms, the nature of the organism and the nature

of the conditions -Also referred to as Nurture and

Nature respectively, terms coined by Richard

Mulcaster in 1581, and later used on the long

opposition debate about the relative importance of

heredity and environment on behaviors- (Pinker and

Pinker, 2014). The nature-nurture controversy is still

ongoing. Biologists have accepted that genes, the

environment, and interactions between them affect

behavioral phenotypes; however, it “retains the flavor

of the nature-nurture dichotomy”, which influences

research in this field (Robinson, 2004). Thus far,

nature and nurture have been raised as two different

phenomena, each one explaining a relative part of

social behaviors. But what if they were both

expressions of the same phenomena at different

scales?

Social behaviors are complex phenotypes

exhibited by individuals that belong to complex

systems, and complex systems deploy through many

spatio-temporal scales. Sociality is composed of

micro-level actions that aggregate to produce meso-

and macro-level phenomena. Depending on how

individuals interact with each other at the micro-

social level, different types of social states can be

produced at the macro-level (Hobson et al., 2019).

To illustrate the importance of scales when

studying complex systems, consider the following

example: if we compare a human and a gas containing

the same number of molecules that are in the human

body, but with no particular arrangement, which

system is more complex? (Siegenfeld and Bar-Yam,

2020).

On a microscopic scale, it is more difficult to

describe the positions and velocities of all the

molecules of the gas than it is to do the same for all

the molecules of the human (thus, the gas seems more

complex). At the human scale, a gas looks quite

simply, because behaviors will only be perceived

when involving trillions of molecules, and there are

few behaviors of gases involving so many molecules

(thus, complexity seems lower). On the other hand,

human behaviors get more complex as the level of

detail increase, “the description will first include the

overall position and velocity of the human and then

the positions and velocities of each limb, followed by

the movement of hands, fingers, facial expressions, as

well as words that the human may be saying”

(Siegenfeld and Bar-Yam, 2020).

If we use the same approach to describe a society,

from macro to micro scale, its description starts on

societies, moving to individuals, individuals are built

from organs and these are composed by tissues, which

in turn are constituted by individual cells. At scales

smaller than that of a cell, complexity further

increases as one sees organelles, followed by large

molecules such as proteins and DNA, and then

eventually smaller molecules and individual atoms.

This incredible multiscale structure is a defining

characteristic of complex systems (Siegenfeld and

Bar-Yam, 2020)

Social scales can be useful to compare sociality

across species, to describe different levels of their

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organization, to highlight the features of sociality

(Hobson et al., 2019), and to better contextualize

studies based on specific social traits.

The advances in genome sequencing and

comparative genomics provide the opportunity to

move forward the nature-nurture debate. Nowadays it

is clear that DNA is both inherited and

environmentally responsive, and that behaviors are

“orchestrated by an interplay between inherited and

environmental influences acting on the same

substrate, the genome” (Robinson, 2004).

4 SMALLER SCALE:

ARCHITECTURE AND

COMPLEXITY OF THE

GENOME

“For complexity at larger scales, there must be

behaviors involving the coordination of many

smaller-scale components“ (Siegenfeld and Bar-

Yam, 2020). According to this statement, to produce

socially complex behaviors, the genome components

must be somehow organized to ensure this

coordination under ever-changing environmental

conditions.

A genome provides all the information the

organism requires to function (Nature education,

2014). The genome has a non-random architecture

(Lynch, 2007b; Wolf, 2003). Koonin (2009) defined

‘genome architecture’ as the totality of non-random

arrangements of functional elements in the genome

(e.g., genes, regulatory regions). This architecture is

not fixed; it is shaped by evolutionary forces like

recombination, mutation rate, and transposable

elements that give place to differences in the genome

architecture (e.g. ploidy levels, gene copy number

variation, chromosomal inversions, and novel genes)

(Gokcumen et al., 2013; Koonin, 2009; Rubenstein et

al., 2019; Yeaman, 2013). The variations in the

genome architecture can be evidenced within the tree

of life; from small and packed genomes with

overlapping genes in viruses (Firth and Brown, 2006),

to compact genomes organized in operons with

intergenic regions and few gene overlap in

prokaryotes (Lillo and Krakauer, 2007; Rogozin et

al., 2002), to genomes with protein-coding sequences

organized in intron-exon structure in eukaryotes

(Lynch, 2007a; Lynch, 2007b).

But how does genome architecture influence the

complexity of the genome? The zero-force law of

evolution states that any evolutionary system with

variation and heredity will tend to diversify and

increase in complexity because these are both

variance quantities that spontaneously increase as

errors accumulate in time (McShea and Brandon,

2010). Michael Lynch’s theory (Lynch, 2007a; Lynch

2007b; Lynch and Conery, 2003) states that genetic

changes that increase the complexity of genome

architecture are slightly deleterious and fixed only

when purifying selection is weak. In large

populations, purifying selection is strong, so the

“complexification threshold” cannot be surpassed,

producing compact genomes. On the contrary,

genomes of small populations (e.g. eukaryotes) are

beyond the threshold, so the complexification of the

genome is possible (Koonin, 2009).

But under which circumstances is complexity

beneficial? According to the Law of Requisite

Variety, an effective system must be at least as

complex as the environment to which it must react. If

a system must be able to provide a different response

to each of the 100 environmental possibilities it is

presented with, then that system should at least

explore 100 possible actions (Valentinov, 2014).

Under this scenario, how is this “complexity

threshold” regulated? Following complex systems

notions, macro-level dynamics emerging from micro-

level interactions can ‘feedback’ to constrain micro-

level interactions and dynamics within the range of

variability that ensures persistence (Hobson et al.,

2019). This negative feedback stabilizes not only

micro and macro-level behaviors but also imprints

tradeoffs between traits at different scales of the

organization.

5 HIGHER SCALE:

RELATIONSHIP BETWEEN

THE COMPLEXITY OF THE

GENOME AND SOCIAL

COMPLEXITY

As social complex behaviors are influenced by

numerous genes (McShea and Brandon, 2010), it is

reasonable to ask how did independent genes evolve

to ensure the level of coordination needed to sustain

sociality? One hypothesis is that genes are clustered

together within a region of a chromosome and

inherited as a single unit, potentially regulated in

concert, as supergenes, such as the case of the non-

recombining portions of the sex chromosomes

(Campagna, 2016; Rubenstein et al., 2019). These

supergenes play a key role in the evolution of

complex adaptive variation (Brelsford et al., 2020).

For example, the White-throated bird coloration and

Social Complex Systems as Multiscale Phenomena: From the Genome to Animal Societies

103

mating behavior are determined by a supergene

(Campagna, 2016), and also is the polymorphic social

organization of Formica ants (Brelsford et al., 2020).

These cluster genes phenomena also admit an

explanation based on complexity theory, which

explains that complexity at large scales requires the

coordination of many smaller-scale components. Due

to this coordination, complexity at a small scale is

limited, because the coordination depends on

interdependencies between the interacting parts

(Siegenfeld and Bar-Yam, 2020)

Sociogenomic studies also support the idea that

behavioral phenotypes are underpinned by clustered

and organized genes, as the so-called "toolkit" genes,

a set of deeply conserved genes that consistently

regulate the development of similar morphological

phenotypes across many species(C. C. Rittschof &

Robinson, 2016; Clare C. Rittschof et al., 2014; Shell

& Rehan, 2019). Toolkit genes related to odorant

receptors ordered in tandem have been found in

several social species such as honeybees and ants,

possibly linked to chemical communication(Kent et

al., 2019). Gene expression studies of vertebrates

suggest the existence of behavioral gene sets for male

polymorphisms (for FoxP2 and its orthologs),

possibly related to speech, song, and other types of

vocalizations. Other studies have found gene clusters

in multiple complex phenotypes, some examples are:

homeobox genes, enzymes of the same metabolic

pathway, olfactory receptors, vertebrate immune

system, and plant-pathogen response(Bear et al.,

2016; Ebstein et al., 2010; Koonin, 2009). Overall,

evidence suggests the convergent evolution of genetic

toolkits, calling attention to its underappreciated role

in the evolution of complex traits(Bear et al., 2016;

Donaldson & Young, 2008; Liu et al., 2016; C. C.

Rittschof & Robinson, 2016; Clare C. Rittschof et al.,

2014).

6 COMPLEXITY ON DIFFERENT

SCALES: TRADE-OFF

BETWEEN GENOME

COMPLEXITY AND SOCIAL

COMPLEXITY

According to “the agency theory” all living organisms

pursue a goal (Walsh, 2015), and to achieve it, they

must find solutions. As unique solutions are rarely the

case, social systems explore simultaneously multiple

solutions and give place to trade-offs that enable

organisms to exploit resources and to respond

differentially to environmental pressures. These

solutions can vary from apparently simple ones to

those that appear more complex (Hobson et al., 2019).

As we discussed in the previous section, the

existence of a tradeoff due to feedback loops between

scales underlies organized complexity. For example,

social animals can exhibit complex behaviors as

organized societies if their genome is organized

enough to enable the orchestration of all the genes

responsible for that behavior. Animals’ complexity

tradeoffs can also explain why some animals become

adaptive while others become efficient (Siegenfeld

and Bar-Yam, 2020).

Adaptability encompasses many independent

actions taken in parallel (a situation in which the

system is overly complex). On the other hand,

efficiency occurs when many parts of a system

manage to work in concert (Giving place to

specialized systems). Because of the tradeoff

between complexity and scale, an adaptable system

can become more complex, but predominantly at

smaller scales, while an efficient system will have a

complexity profile with lower complexity but

extending to larger scales (Siegenfeld and Bar-Yam,

2020).

Following the later statement, it is timely to

rethink our current framework to classify social

complexity, as it is now reasonable to say that solitary

animals fit in the description of adaptability, while

group-living animals appear as efficient systems. The

key idea is that when speaking about complexity we

must consider the scale. To this end, we cannot assert

that a solitary animal is less complex than a group

living organism; instead, we could say that they are

both complex at different scales. This could help

reconcile the lack of consensus in the definitions and

findings throughout studies of social complexity. To

prevent this from further happening, studies on

animal social complexity should make explicit the

scales at which social traits are being studied and

measured.

7 CONCLUSIONS

Social complexity is a problem of organized

complexity that must be approached as a multiscale

phenomenon.

This approach considers multiple scales and

interactions across components of the system and

enables to study the phenomena as a whole. In our

view, this way may get us closer to address questions

such as: What is social complexity? Can social

complexity be measured? Are some animals more

COMPLEXIS 2021 - 6th International Conference on Complexity, Future Information Systems and Risk

104

socially complex than others? And if this is the case,

why?

Social complexity studies are becoming more

frequent, and a strong theoretical framework is

needed to integrate new findings. We believe that a

good approach is to work towards a theory of social

complexity that integrates concepts of complex

systems, behavioral ecology, and social systems.

Also, future animal social studies should include

the molecular scale -e.g., the complexity of genome

architecture- which allows comparing social

complexity, within and between species, because this

scale of organization is shared by all living

organisms. Comparative genomics and systemic

approached simulations might be helpful to answer

the mentioned questions and to improve our

understanding of social complexity.

ACKNOWLEDGEMENTS

Funding: ANID FONDECYT 1170995 and

Universidad Mayor Doctoral Scholarship.

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