De-emergence:

Experimental Approaches to Deactivate Processes of Emergence

GIANFRANCO MINATI

Italian Systems Society,

Via Pellegrino Rossi, 42,

ITALY

Abstract: - The processes of self-organization and emergence have been intensively studied and modeled. The

focus has been on their generative mechanisms and the need to preserve and sustain their continuously acquired

coherence(s), for instance, in ecosystems and living systems. Rarely has the focus been on the reverse attitude,

that is, to prevent and avoid their establishment or on approaches leading to their deactivation. This is probably

because of their supposed fragility since they are considered easy to break down with perturbations. For

instance, a flock may be destroyed by shooting inside it, or an ecosystem may be ruined by placing poisonous

substances within it, such as the case of an anthill or weed killer onto a lawn. Here, we consider the occurrence

of unwanted and dangerous cases of self-organization and emergence against which there are currently no

effective approaches available and, thus, need to be appropriately modeled and implemented. For example, the

establishment of tornadoes and hurricanes. The latter is known as Rayleigh-Bénard convection can easily be

deactivated in laboratory conditions, but not once established in the atmosphere because of the power of the

created forces, which generate destruction and devastation. We are interested both in the theoretical aspects of

such eventual de-emergence approaches and in their actual technical implementability.

Key-Words: - Coherence; Deactivation; Decoherence; Emergence; Incompatibilities; Incompleteness;

Prevention; Systemic Domain.

Received: April 19, 2024. Revised: October 21, 2024. Accepted: November 23, 2024. Published: December 31, 2024.

1 Introduction

The purpose of this article is to focus on the unusual

topic of de-emergence, which involves the

deactivation of processes and properties of

togetherness, such as emergence and self-

organization. This is also in reference to the theme

of reverse emergence, which is understood both as

the effects of the emergence process on the

phenomenon from which it emerges and as a study

of its dismantling.

This article brings to attention the research topic

of deactivation of processes that constitute

togetherness in populations of elements and,

possibly, their interactions. Examples of conditions

and processes that constitution togetherness are

covariance and correlation, (long-range) correlation,

coherence, ergodicity, interactions, meta-structural

properties, networking, power laws, remote

synchronization, self-similarity, and synchronization

between them. We consider the cases of self-

organization and emergence for which deactivation

becomes problematic when they assume forces and

time scales that are difficult to deal with, such as

tornadoes and hurricanes.

The relevant theoretical aspects and admissible,

practical approaches are considered, to activate

related lines of research. In Section 2, some

ingredients for the togetherness of collective

entities, generalized as multiple systems, such as

covariance and correlation, are specified. In Section

2.1, we consider the introductory issue of Multiple

Systems as given by clustering and synchronization.

In Section 2.2, we elaborate on Multiple Systems as

Collective Beings. In Section 2.3, the specific cases

of self-organization and emergence are mentioned.

In Section 3, we address the case of deactivation of

togetherness. In Section 3.1, the case of decoherence

is addressed in particular. We then mention related

possible theoretical issues that need to be explored

and extended. This is in reference to the theme of

reverse emergence, understood both as the effects of

the emergence process on the phenomenon from

which it emerges and as a study of its dismantling.

In Section 3.2, related theoretical issues are

mentioned. In Section 3.3, two real cases are

considered: the Rayleigh–Bénard convection and

social systems, together with some possible

tentative approaches for their deactivations. This is

to provide an idea of the admissibility of the

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problem and possible approaches. It is, therefore, a

research project open to new approaches and

modeling. In Section 3.3.1, possible experimental

approaches for the deactivation of Rayleigh–Bénard

convection, such as tornadoes and hurricanes, are

mentioned. In Section 3.3.2, possible experimental

methods for the deactivation of emergence and self-

organization phenomena in social systems are

considered. We conclude by mentioning the related

open lines of research that could be introduced.

2 Some Ingredients for the

Togetherness of Collective Entities

With the term “togetherness,” we refer to the ability

of generic entities to acquire specific properties

having the effect of making them unified, for

instance, when structured and interacting

reciprocally, [1].

At the classic macroscopic level, the

togetherness of material entities composed of

elements, considered at different levels, is due to

their generic structures, such as structured

components of electronic and mechanical devices,

molecular bonds, and attractive forces, such as

magnetic and gravitational.

An introduced significant generalization, [2],

relates to meta-structures, multiple, simultaneous

structures, variable over time, and their sequences as

in liquids and in the organization of domains within

a ferromagnetic material, [3]. We mention a case

relating to meta-structures, such as links among

links of a network, establishing, in turn, multiple

superimposed soft networks, [4]. Moreover,

sequences of multiple structures over time are

considered meta-structures of interest when

coherence establishes multiple, partial systems, [5].

The large varieties of interactions (occurring when

one’s behavior affects another’s behavior) involved

are analytically intractable and impossible to

represent in explicit ways. A previous approach was

based on using mesoscopic variables [6] that are not

related to microscopic properties but to

equivalences, allowing for incompleteness and

reasons for unpredictability.

A further generalization occurs when

considering collective entities whose acquired

togetherness is due to their continuous interaction,

enabling levels of coherence. The interest here is in

collective interacting entities of which the typical

basic example is represented by the so-called

Brownian motion, the irregular, disordered, random,

and unpredictable motion of a speck of pollen on

water due to collisions with single and multiple

water molecules, interacting with one another

because of thermal energy. Due to thermal

interactions a number of particles subject to

Brownian motion in a given medium, such as water,

have not preferred directions for their random

oscillations. Consequentially, over a period of time,

the particles and molecules will tend to be spread

evenly in the medium.

This situation is particularly generalized

considering multiple systems established, for

example, by the multiple roles of their interacting

components such as in the case of multiple

interactions, [7]. Their structure is constituted by the

dynamic occurrence of multiple and variable

interactions involving the same elements, as in

ecosystems and the internet, and multiple networks

where the same nodes simultaneously belong to

different networks (such as energy and

telecommunications networks); keeping, however,

dynamic levels of predominant coherences or

replaceable temporary incoherences corresponding

to the established systems. This is the dynamics, i.e.,

the mechanisms, of processes of emergence of

complex systems, [8].

As is well-known, the classic model of non-

multiple systems may be considered as being given

by systems of ordinary differential equations such

as:

dΜ1/dt = f1 (Μ1, Μ2, …, Μn)

dΜ2/dt = f2 (Μ1, Μ2, …, Μn)

……………………………. (1)

dΜn/dt = fn (Μ1, Μ2, …, Μn),

(1)

where:

- the considered system is intended as constituted by

n elements pi (i = 1, 2, …, n), assumed invariable

in number and properties (i.e., a very unrealistic

assumption);

- there exist measures Μi (i = 1, 2, …, n) for each n

element pi (i = 1, 2, …, n);

- the n elements pi (i = 1, 2, …, n) interact between

them through fixed rules of interaction fn.

As a consequence, the change of any measure

Μi is, therefore, a function of all other Μs. The

change of any Μ implies a change for all the other

measures, making the system a single, totally

interconnected whole. The instantaneous values of

Μ1, Μ2, ..., and Μn specify the state of the system at

each instant.

Examples include systems such as electronic

devices consisting of

- components pi (i = 1, 2, …, n) such as, for

example, capacitors, diodes, inductors, micro-

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CPUs, micro integrated cards, oscillators, relays,

resistors, and transistors;

- having properties (Μn) such as measurements of

their electrical state, thermal state, dissipation

level, and the result of microprocessing of input

signals;

- interacting with each other via multiple circuitry

(fn) when powered, involving for example

multiplying or reducing the input signal or their

combinations, approximating the maximum or

minimum value of the input signal or their

combinations, combining multiple signals, sending

signals conditioned by the state of others.

The acquired systemic property is functioning

(the device becomes a computer, robot, smartphone,

television) that decays when the fn interactions

cease, for example when the power supply ceases.

Other examples include the solar system, and

the hydraulic and electricity systems.

Examples of generic systemic properties are a)

allostasis -when keeping stability through structural

changes-, b) resilience -when adapting and self-

repairing in the face of disruptive perturbations-,

and c) autopoiesis -when having the ability to

regenerate recursively-.

Temporal sequences of the system (1) specify

the behavior of the system, the assumed constituted

fixed and invariable elements, and the rules of

interactions.

Instead, the simultaneous validity of different

versions of the system (1) may be intended to

analytically represent the concept of multiple

systems, when fn changes in fn,t and the expressions

in the system (1) become time-dependent, i.e.:

dΜ1/dt = f1, t (Μ1, Μ2, … Μn)

dΜ2/dt = f2, t (Μ1, Μ2, … Μn) (2)

…………………………….

dΜn/dt = fn, t (Μ1, Μ2, … Μn)

(2)

Realistically, it is possible to have a different

number of variations per instant, and the number of

variations may be different, even if limited per

instant. It is then possible to consider the level of

multiplicity by considering the number of variations

over time and the properties of its trend over time,

identifying levels of multiplicity.

Examples include systems such as audience,

baseball teams, company staff, customers of a shop,

passengers on trains or planes, and school classes

where:

- components pi (i = 1, 2, …, n) as agents, for

example, players of teams, company staff,

passengers on trains or planes, boids of flocks,

swarm insects, molecules of biological entities;

- having properties (Μn) such as measurements of

their state, for instance, 3D spatial position,

velocity, direction, temperature, and mass;

- interacting with each other via the application of

global and local variable, context-dependent

(compositions of) rules (fn,t) such as for collision

avoidance based on some feedback mechanisms

and compliance with minimum permissible

distance values; compliance with maximum

permissible distance values to avoid disintegration;

adoption of a kind of dependent behavior such as

analogous imitation of that of the adjacent

neighbors; and self-regulatory, adaptive behavior

through some learning mechanism.

The acquired, emergent systemic property is the

behavior and its consistence, ability to restore

temporary losses of coherence, and tolerate

temporary inconsistencies (for instance of baseball

teams, company staff, and communities keeping

their identity in the face of variations in the number

of components and in the interaction rules fn,t

applied).

2.1 Multiple Systems as Clustering and

Synchronization

We mention the possibility of considering Multiple

Systems as dynamic clusters having synchronization

as the source of their coherence, [9].

For example we may consider the case of

populations of interacting clocks, whose internal

cyclic dynamics are given by

Φ = ω (3)

where:

- Φ is the phase;

- ω is the frequency.

A simple case is given by large populations of

fireflies which, when synchronized, generate large

amplitude periodic signals. The equation of the

Synchronization Function between them can be

found, see, for instance, [9].

We mention another approach [9] based on

considering the dynamical law modeling the time

evolution of a generic unit considered as a logistic

map suitable to represent realistically population

dynamics:

f(x)=1-αx2 (4)

where:

- x is the number of elements;

- α is a suitable control parameter.

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In reference to the concept of Multiple Systems it is

possible to consider the system of N globally

coupled logistic maps and study their possible

mutual synchronizations during their interaction:

N

Xi(t+1) =(1-ε) f(xi(t)) + ε Ʃ f(xj(t)) (5)

N j=1

where

- i = 1,2, ..., N represents single logistic maps f(x) =

(1-ax2);

- f(x) is the equation f(x)=1-αx;

- α is a control parameter for the logistic map. It

shows chaotic-like behavior when α > 1.401...;

- ε is the coupling strength.

Coherently operating groups are intended as

establishment of dynamic clustering. In this case the

dynamic clustering is observed in the interval 0.32

> ε > 0.075. Mutual synchronization, i.e., the

coherent phase, of the entire ensemble manifests for

ε > 0.32. When α = 1.8, dynamical clustering occurs

in the interval 0.37 > ε > 0.14, and so on [9].

Furthermore, it is possible to consider

approaches for clusters and synchronization in

Dynamic Networks established by populations of

interconnected elements having simple internal

dynamics. We consider here the case when elements

consist of N identical logistic maps: “The pattern of

connections in the network is specified by a random

graph with the adjacency matrix Tij which is

obtained by independently generating any possible

connection with a fixed possibility υ.”, [9].

The collective dynamics of the network are

modelled by the following equation (6):

N N

Xi(t+1)= 1-[ε/υ(N-1)] Ʃ Tij f(xi(t)+ε/[υ(N-1)] Ʃ Tij f(xi(t))

(6)

j=1 j=1

where symbols are as specified above. When υ = 1

equation (6) coincides with (5) describing the

collective dynamics of N globally coupled logistic

maps.

Through numerical simulations [10] it has been

shown “...that, when the coupling straight ε is

gradually increased, these networks experience

dynamical clustering and synchronization.” [9, p.

250].

We stress, however, that the limited

effectiveness of these models lies in the fact that

they are mainly based on fixed interconnections

allowing for the occurrence of the phenomena of

emergence limited to clustering and

synchronization.

2.2 Multiple Systems as Collective Beings

We now mention how multiple systems may reach

levels of temporary stability and robustness and as

well to constitute initial conditions such as multiple,

partial, tentative, and failing conditions. These may

possibly converge to the establishment of a

collective system when the collective interaction

acquires significant levels of coherence: long-range

correlations as in processes of self-organization and

emergence (Section 2.3).

Furthermore, when the elements are

autonomous, i.e., provided with a cognitive system

making them able to decide rather than compute

(selecting among possible reactions or looking for

optimizations) their behavior, multiple systems are

called collective beings, [7] (Figure 1).

Fig. 1: An illustration of the concept of multiple

systems as collective beings where same elements

belong to more systems

The concept of collective being particularly

applies to the collective behavior of agents assumed

equipped with a cognitive system, having,

furthermore, the ability to use different cognitive

models depending on contextual situations, to

memorize and learn, and emulative abilities, e.g.,

anthills, herds, schools of fish, swarms, and social

systems such as Internet users, markets, football

teams, players on the stock exchange, and traffic

jams.

In multiple systems and collective beings, the

coherence between elements replaces structures.

The coherence of collective beings is due to

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multiple, variable, differently necessary, and

sufficient levels of properties, such as:

 Interchangeability among components, given by

the ability to take on the same roles at different

times or different roles at the same time as, for

instance, in (quasi) ergodic behaviors. We

mention that components of populations are

assumed to assume ergodic behaviors in case

they are related in such a way that, when, at any

moment in time x% of the population is in a

particular state, then each element of the

population is assumed to spend x% of time in

that state. Realistically, instead of this, it is more

appropriate to consider levels of percentages of

elements spending x% of time in that state,

which allows to establish degrees of ergodicity.

 Multiple roles, e.g., the position and behavior of

a single animal in multiple collective animal

behavior, affect (interact with) those of other

ones belonging to different systems, e.g.,

ecosystems, and, in networks, the same node has

multiple interconnections with other nodes. We

mention how, usually, the multiplicity of the role

of a component is not due to its direct actions on

the other ones, but to the different behavioral

variations that the other ones assume depending

on the position and behavior of the component in

question.

 Multiple mediated flows of information having

no direct, linear conveyance of information, such

as when non-spatially close components in

collective behaviors, e.g., boids of flocks, have a

suitable topological distance [11] and in remote

synchronization, [12], [13]. This is the case of

generic collective animal behaviors:

“Correlation is the expression of an indirect

information transfer mediated by the direct

interaction between the individuals: Two animals

that are outside their range of direct interaction (be

it visual, acoustic, hydrodynamic, or any other) may

still be correlated if information is transferred from

one to another through the intermediate interacting

animals”, [14].

Multiple systems consider the weak micro-

dynamics that are typically assumed to be irrelevant

and ignored since it is presumed that they are

overbeared by predominant macroscopic behavior.

Significant and decisive for their establishment is

the emergence of collective behavior. The

macroscopic behavior of multiple systems is

emergent, while it is supposed to be suitably

approximated by the sum of micro-dynamics, it is

non-summable because of their varied natures.

At this point, we may consider how the acquired

togetherness of collective components may be

considered as given, for instance, by the variable

occurring of differently necessary and sufficient

levels of (eventually combined) properties, such as

their belonging to the basin of an attractor, be

subject of covariance and correlation, (long-range)

correlation, coherence, ergodicity, interactions,

meta-structural properties, networking, power laws,

remote synchronization, self-similarity, and

synchronization between them. We may specify

some approaches to determine covariance,

correlation, measures, and generalizations as the

cross-correlation function.

The concept of correlation is closely related to

that of covariance since both measure the

dependence between the variables under

consideration. In particular, covariance determines

how two variables covary.

It is possible to use correlation measures

applying, for instance, linear approaches such as the

so-called Bravais-Pearson coefficient, [15], [16].

The Bravais-Pearson coefficient measures the linear

correlation between two sets of data such as

between newborns’ weight and length and a person's

age and their corresponding income in a place.

The covariance [17] of two variables is divided,

however, by the product of their standard

deviations.

The covariance identifies how two random

variables X and Y covary, that is, at what level both

change in the same way. The Bravais–Pearson

coefficient is essentially its normalized

measurement between −1 and 1. We must stress that

the Bravais–Pearson coefficient, as covariance

itself, measures only linear correlations and ignores

other types of relationships, [18], [19], [20].

Considering in a population a pair of random

variables (X, Y), the Pearson’s correlation

coefficient ρ is given by:

 

( , )

, Cov X Y

XY XY





(7) (3)

where:

- Cov is the covariance,

- σX is the standard deviation of X,

- σY is the standard deviation of Y.

The covariance is given by the following:

( )( )

( , ) x x y y

Cov X Y n





(8)

where:

-

x

and

y

are the means of the data series,

- n is the size of the considered sample.

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The Pearson’s correlation coefficient ρ in the (7)

may be expressed as r in the formula (9) where n is

the number of observations:

n(Σxy) – (Σx)(Σy)

r =



[nΣx2 – (Σx)2][nΣy2 – (Σy)2]

(9)

However, the Bravais–Pearson approach can be

generalized by other linear measures, such as the

cross-correlation function. This applies to two time

series of the same length N and where values are,

respectively, denoted by xn and yn. Such values have

been previously normalized, and have a zero mean,

and a unitary variance. In this case, the cross-

correlation function CXY (τ) depends on time lag τ

and varies within the range from −(N−1) to N−1

according to the following expression:

1

10

( ) .

( ) 0

N

nn

n

XY

x y if

CN

C if



















(10)

(5)

The cross-correlation values can vary from 1

(maximal synchronization) to −1 (loss of

correlation).

A successive, further generalizing level is given

by the occurrence of processes of self-organization

and emergence as sequences of covariances,

correlations and, more generally, variable but

predominant coherences, as considered in the

following section.

In conclusion, let us consider how systems

manifest themselves as a whole thanks to the

appropriate interaction of their parts (different

partitions are possible at different levels of

description and scalarity, e.g., in a living system we

can consider cells, neurons, organs, and tissues)

when establishing significant levels of cross-

correlation between them. Interacting alone is not

sufficient as for the so-called Brownian motion

mentioned above. They do not constitute a system,

i.e. they are not cross-correlated and do not acquire

systemic properties.

In multiple systems, there are phenomena of

corresponding multiple cross-correlations albeit at

different levels of intensity.

In the end we mention how autocorrelation is

given by the degree of similarity between a given

time series and its lagged version, considered over

successive time intervals. Autocorrelation consists

in measuring the relationship between a variable's

current value and its past values.

2.3 Cases of Self-Organization and

Emergence

We now mention two cases in which the collective

interacting entities acquire forms of togetherness

and robustness, i.e., the processes of self-

organization and emergence (Table 1) to the point of

constituting collective entities with their properties,

different from those of the constituent elements. In

systems science, the topic of emergence, a term

originally coined in [21] and self-organization,

introduced in [22] are found in an enormous amount

of literature and research, [23], [24], [25], [26], [27],

[28].

Table 1. Conceptual comparison between self-

organization and emergence

Self-organization

Emergence

Synchronization

Periodicity

Self-similarity (at any scale a

geometrical object is similar to a

part of itself. Iteration.

Multiple variable synchronized

synchronizations

Multiple periodicities

Coherence is the property of

collectively interacting elements

to acquire and maintain emergent

properties. Continuity.

Next, we examine how self-organization can be

considered to consist of the (regular or quasi-

regular, allowing tolerance for local and temporary

deviations) recurrent acquisition of coherent

sequences of variations of the same property.

Examples of self-organization include self-

organized properties of phenomena, such as the

acquisition of whirlpooling in liquids and the

repetitive flying of swarms, e.g., mosquitos around

light. Furthermore, we mention the so-called

Rayleigh-Bénard convection [29], in which liquids

are evenly heated from below, which consists of the

formation of convective patterns. While the

occurrence of convective patterns may be

predictable only in incomplete ways, other

properties, such as the acquired patterns and their

directions, are not predictable at all [30]. We also

mention the so-called Belousov-Zhabostinski

reaction, [31], [32], which consists of oscillating

chemical reactions that acquire synchronized,

periodic variations of striking color.

We now mention how emergence, [33], [34],

[35], [36] can be considered to consist of the

(regular or quasi-regular, allowing tolerance for

local and temporary deviations) recurrent

acquisition of coherent sequences of variations of

structurally different, however admissible,

compatible, and equivalent, properties. For instance,

the change of position, speed, and direction of a

boid in a flock, occurring at the time tn+1, must be

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admissible and compatible with the physical

constraints of the generic boids at the time tn, e.g., a

boid cannot instantaneously multiply its speed, [11],

[27]. As in the processes of self-organization,

composing elements of the established collective

entity have several equivalent instantaneous changes

available to choose from, where the selection occurs

in several possible ways, such as due to energetic

convenience, fluctuations, and cognitively-based

living collective beings as flocks and herds,

respectively, that is due, for instance, to atmospheric

conditions and the shape of the territory.

Furthermore, changes must allow for the

acquisition of coherent states or temporary

incoherent recoverable incoherences [8], of which a

basic understanding is the ability to keep different

admissible, compatible, and equivalent versions of

the same property, even if this understanding is

observer-dependent. For example, the observer

recognizes the same flock even moving through

different shapes, densities, altitudes, and number of

components (when is a flock no longer a flock?).

This relates to the character of prevalent uniqueness

and unrepeatability of self-organization and

emergence processes. We may consider emergence

as constituted of coherent sequences of multiple,

local, and temporary self-organization-like

collective entities and processes, [37].

3 Deactivating Togetherness

We now consider the following concepts, inspired

by the so-called still, in progress, the idea of reverse

emergence, [7], [38], [39].

The processes of self-organization and

emergence have been intensively studied and

modeled. The focus has been on their generative

mechanisms and the need to preserve and sustain

their continuously acquired coherence(s), for

instance, in ecosystems and living systems. Rarely

has the focus been on the reverse attitude, that is, to

prevent and avoid their establishment or on

approaches leading to their deactivation. This is

probably because of their supposed fragility since

they are considered easy to break down with

perturbations. For instance, a flock may be

destroyed by shooting inside it, or an ecosystem

may be ruined by placing poisonous substances

within it, such as the case of an anthill or weed killer

onto a lawn.

We consider the occurrence of unwanted and

dangerous cases of self-organization and emergence

against which there are currently no effective

approaches available and, thus, need to be

appropriately modeled and implemented. For

example, the establishment of tornadoes and

hurricanes. The latter are known as Rayleigh-

Bénard convection that can easily be deactivated in

laboratory conditions, but not once established in

the atmosphere because of the power of the forces

that are created, which generate destruction and

devastation. We are interested both in the theoretical

aspects of such eventual de-emergence approaches

and in their actual technical implementability.

3.1 Decoherence

We notice how we may consider self-organization

and emergence as processes necessarily based on

coherence, as given by synchronization and

correlation. In this work, we consider decoherence

as a loss of coherence and synchronization in the

classical world (in quantum physics, decoherence

has a very different meaning). The process of losing

coherence is usually considered to have negative

aspects, involving the loss of (systemic) properties

acquired thanks to the establishment of coherences,

e.g., degenerations of collective behaviors such as

the dispersal of a flock into uncorrelated boids. The

process of losing coherence is assumed to have a

degenerative nature. Much attention has been paid

to how to maintain or recover coherence, e.g., in

dissipative structures and processes of emergence.

Here, on the contrary, the general purpose is

related to approaches suitable to prevent and

deactivate establishing or established processes of

self-organization (recurrent acquisition of coherent

sequences of variations of the same property) and

emergence (recurrent acquisition of coherent

sequences of variations of the same and different

properties), networks, and systemic properties. In

summary, the interest is in the processes of de-

emergence.

We focus on the coherence acquired by negative

processes such as having a degenerative nature, the

consolidation of pathological states, and acquired

pernicious collective behaviors. The interest is, for

instance, on the prevention and deactivation of

incoming illnesses due to supposed processes of

emergence, the emergence of economic problems -

of which the so-called tulip crisis type-like (1637)

was a typical example-, hurricanes, unwanted

collective behaviors, e.g., traffic congestions,

invasive, and dangerous ecosystems. We spotlight

approaches that are suitable to prevent and

deactivate processes of self-organization and

emergence, [7], i.e., anti-self-organization and anti-

emergence, and to prevent and deactivate the related

establishment of logical openness [7] and theoretical

incompleteness [7] based on unpredictable

occurring of equivalences.

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From a theoretical point of view, it is about

eliminating the conditions necessary for the

establishment of emergence phenomena such as

logical openness and theoretical incompleteness.

This is because logical closure and completeness are

enemies of complexity, which they reduce to

structures, like collective behaviors to marching

platoons.

We mention how the concept of logical

openness is an extension of thermo-dynamical

openness (Table 2).

Table 2. Conceptual comparison between close

systems and logical open systems

Logical Closed Systems

Logical Open Systems

Deterministic

Nondeterministic

Context insensitive

Context-sensitive

Nonlearning

Learning

Object-oriented

Process-oriented

Nonflexible

Flexible

Fixed rules, variable

parameters

Changing rules

Contradiction avoiders

Using contradictions at a

higher level of

description

Mono or non-dynamic

strategies

Multi-dynamic strategies

Ideal Modeling

- Deterministic chaos

equations

- Equations of mechanics

- Equations of

thermodynamics

- Ergodic systems

- Field equations, such as

those of Maxwell’s

electromagnetic field

- Network science (ideal

scale-free networks)

Non-Ideal Modeling

- Agent-based models

- Artificial life

- Cellular automata

- Dissipative structures

- Neural networks

- Properties of big data

Fixed structures

Variable structures, e.g.,

ecosystems

Nonadaptive

Adaptive

The logical closure of modeling relates to the

evolution of closed systems, having no exchange of

matter-energy with the environment, that can be

represented with:

- Formal and complete analytical representations

of the system’s state variables and their intra-

relationships, are assumed to all available;

- Complete analytical representations of

interactions with the environment are assumed

available.

The knowledge of the two points above allows

us to deduce all possible states that the system can

assume.

Conversely, the logically open modeling or

logical openness [40] is given by the violation of

one or both of the two points above. We may

consider logical openness as the occurrence of an

infinite number of degrees of freedom for the

system. This requires the use of multiple, variable,

equivalent, and non-equivalent models. These are

violations of the two points above regarding logical

closure. However, the unlimitedness of the degrees

of freedom is a necessity for the constitution of

emergence processes that would otherwise have

fixed structures, substituting coherence.

The concept of theoretical incompleteness, [41],

[42] relates to its non-completability in principle

and it is related to logical openness since:

- a single model is assumed not sufficient to

represent the phenomenon;

- the degrees of freedom, the system variables are

continuously acquired and vary in number;

- the system continuously acquires equivalent and

non-equivalent properties;

- the system can assume many equivalent and

non-equivalent states, selected, for instance, by

fluctuations. Examples include in quantum

mechanics the uncertainty principle when

accuracy in measuring one variable is at the

expense of another); in theoretical physics the

complementarity principle between wave and

particle natures; and Gödel’s incompleteness

theorems, [43].

In sum the incompleteness of logical openness

lies in the use of a variable number of non-

equivalent models.

However, the typical conceptual context to

which the concepts of logical openness and

theoretical incompleteness are applied is that of

complexity referring to phenomena in which

emergence occurs, as previously considered in

section 2.3; in chaotic systems highly sensitive to

the initial conditions such as the double pendulum

(Figure 2), smoke diffusion and weather,

characterized by the morphologies and properties of

the attractors established by their evolutionary lines

in the phase space and their basins [44]; and in

complex networks [45], [46], [47] acquiring

properties such as the emergence of small words,

clustering measured by the clustering coefficient,

degree distribution, multiplicity and variability of

the nodes and intra connectivity, power laws, scale

invariance, and self-similarity.

In the cases of complexity, zipped, complete,

and explicit models are conceptually not possible

because of the logical openness and theoretical

incompleteness, given by the varieties of

interactions and structures involved.

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Fig. 2: An illustration of the double pendulum

In order to induce, obtain reduction up to loss of

coherence, it is a matter of reducing more and more

the degrees of freedom and the incompleteness, for

example by slowing down interactions.

We may consider, for instance, to proceed via

approaches such as environmental changes,

introduction of perturbations, combinations of

incompatibilities, delays, (environmental)

inhomogeneities, desynchronizations, reducing

equivalences, removal (invalidation) of necessary

conditions, inverting local and global properties, and

prevent remote synchronizations. Examples include

the insertion in the process of desynchronized

acoustic and electromagnetic waves, irregular

vibrations, incoherent predominant entities, particles

with irregular behaviors, irregular light variations,

environmental inhomogeneities, environmental

constraints, incompatible behaviors as inserting

another antagonistic or dispersive collective

behavior, in case artificial, and corrupt the

established systemic domain. It is a matter of

extinguishing or deactivating (cross-) correlations

also through (irregular and inhomogeneous)

environmental temporal and spatial deformations.

Another hypothetical approach would be to

leave the processes unchanged but to reduce their

intensity, making, for instance, the environment

highly dense and sticky. This approach, at first

glance, seems impractical when there are intense

forces and very small metric and temporal

scalarities are at play. Reversely, we mention how

the know-how to deactivate emergencies and

networks also involves knowledge regarding the

ability to recognize deactivation processes in

progress (in case they are fraudulent or malignant)

and recovery capabilities.

3.2 Theoretical Issues

Here, we list some theoretical issues related to

future possible models finalized to deactivate or

extinguish various forms of togetherness. Those

models should be identified, for instance, as:

• Anti-emergence factors, such as strategies and

approaches, as follows:

- reducing incompleteness, e.g., reducing

equivalences,

- operate with (forced) finite values,

- reducing options,

- approximate, converge unruled processes to

ruled,

- combine antagonistic, incompatible emergence

processes,

- transform and use, i.e., reduce, non-ideal model

to ideal (zip in analytic representations).

• Anti-chaotic factors are achievable by reducing

high sensitivity to initial conditions (by forcing,

for instance, the usage of macro initial conditions

made of indistinguishable micro initial

conditions at a different scale).

• Deactivation of networks, identifying structural

critical points or disruptive interventions aimed

at deactivating network functions or its

properties, e.g., identically connecting all the

nodes, making, for instance, the environment

conducive and inconsistent the small-scale

worlds.

• Deactivation of systemic properties reduced to

possessed properties.

Symptoms of the establishment of the processes

of emergence or, in any case, processes compatible

with it (quasi-emergence), such as the establishment

of self-organization zones that can combine and

amplify, extend themselves, converging on a single

process of emergence, or expire out due to their

insufficient compatibility or mutual incompatibility.

See, for instance, the interactions between two

adjacent convection rolls in turbulent Rayleigh–

Bénard convection, [48]. Another example of

symptom occurs when the corresponding acquired

dynamic structures in processes of emergence may

be understood as the occurrence of possibly multiple

simultaneous sequences of processes of self-

organization and are coherent, i.e., display the same

property in spite of adopting multiple coherences.

An example is given by the theory of “dual

evolution” for adaptive systems, introduced in [49].

Finally, let us mention how the situation of

togetherness, as of self-organization and emergence,

is considered special in a context in which the

natural prevalence of disaggregation, incoherence,

and non-synchronization is taken for granted.

However, this situation could be considered not

so obvious as natural, for example when

disaggregation could be considered predominantly

as degenerated togetherness and not just as a

generic disorder where we consider the transition

from disorder to order, [23]. In this perspective,

disaggregation could have prevalent residues of

●

🔴

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previous togetherness that can be considered as, at

least partially, recoverable, composable, and

reactivated. In essence, can togetherness be

considered as, albeit variably, predominant and

detectable in its residues or is there an invariable

predominance of disaggregation?

The question is of interest considering that the

initial conditions for the establishment of the

coherence of togetherness would not be null, the

acquisition of togetherness would not start from

zero. It would be a matter of detecting residues of

previous, possibly remote, togetherness and

exhuming them in new processes, or paying the cost

of going against them.

3.3 Two Cases: Rayleigh–

Bénard Convection and Social Systems

Rayleigh–Bénard convection is a type of natural

phenomenon that occurs in a planar horizontal layer

of fluid that is heated from [50], [51], Figure 3.

Such convections are easily deactivated in the

laboratory, introducing obstacles and perturbations,

while not in the natural environment where

enormous forces and quantities are involved that

have highly unpredictable onset and behavior.

cold

hot

Fig. 3: Schematic example of Rayleigh–

Bénard convection

Collective social systems are kinds of multiple,

collective beings that can be deactivated on a small

scale, like anthills and wasps’ nests. Analogous is

traffic in cities, crowds of people entering shops,

and possibly small riots. At much larger scales,

using the same approaches with greater intensity

and force often does not work and may even be

counterproductive about reinforcing effects. It

should be mentioned how simplified togetherness of

temporary social systems is established by the

occurrence of positive and negative feedback, for

example, in terms of communities of buyers and

sellers in the stock exchanges. Deactivating actions

that are usually assumed include temporary

suspensions of negotiations.

3.3.1 Experimental Approaches to Deactivate

Rayleigh–Bénard Convection

Large-scale Rayleigh-Bénard convection, which

includes tornadoes and hurricanes that have highly

unpredictable onset and behavior, is endowed with

vast forces that involve enormous masses of liquid

and atmosphere, develop at great speed, and have

high levels of unpredictability. Any reproduction of

laboratory approaches is, in fact, impractical. It is a

question of considering the possible use of

approaches of a completely different nature

appropriate to the forces and times of the

phenomenon.

Hypothetically, suitable approaches to deal with

the extreme forces and temporal characteristics of

such phenomena rely on the flexibility and adequate

self-adaptive pointing of appropriate optical and

radiant technologies, with sufficient local temporal

persistence to have thermodynamic effects. For

example, high-power laser radiations, according to

methods and approaches to be studied, e.g.,

introducing desynchronizing local, sparse delays,

phase shifts, as hypothetically represented in Figure

4 and identified with appropriate research activity.

Fig. 4: Schematic example of laser desynchronizing

local, sparse radiations

This would involve adapting, for example,

military devices based on the use of lasers for such

an application, [52], [53], [54], [55].

It would be a matter of moving beams of

radiation along with maintaining the radiation

adaptively constant for a sufficient time to break

down the coherence of the convection in various

parts by superimposing and inserting the adaptive

radiation. Thermodynamic interventions aimed at

producing effects are rather unlikely due to the

limited time scale. Laser-based approaches can be

studied in the laboratory at small scales. The

adequacy of the radiation delivery requires great

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mobile flexibility of the stations equipped with the

sources, providing that it takes advantage of the fact

that it does not require improbable and impossible

proximity to the phenomenon to be deactivated, but

rapid capacity and adjustment of pointing. The use

of drones seems unthinkable due to the limited time

scale within which one must act.

Furthermore, it must be kept in mind that the

radiation passing through the phenomenon to be

perturbed must not impact sensitive entities that

would be seriously damaged, such as ships or

coasts. Other possible ideally considered approaches

involve deactivating initial conditions by using the

same laser-based technologies. However, such

approaches seem practicable only through the

detection of the relevant phenomena, for example,

with satellite and aerial surveys.

3.3.2 Experimental Approaches to Deactivate

Social Coherences

Traffic models, [56], [57], [58], show that traffic

jams can occur spontaneously for homogeneous

traffic flow when the density of vehicles exceeds a

particular value. Traffic can be modeled as

occurring in a number of states, such as free-flowing

and traffic jams, with phase transitions occurring

between the states. Changes in traffic properties,

such as density, flow, and speed, can be induced by

temporary changes in the width of road lanes, by

signs, and the introduction of traffic police vehicles

having the effect of order parameters.

Examples of general approaches used include

disruption and perturbation of information

exchange, e.g., information distortion (e.g., fake

news) of the interaction processes, perturbances,

invasive environmental changes, the introduction of

entities with destabilizing behavior, and acting on

the density.

Never before has it been so necessary to

consider the unethical nature of such approaches,

not only by avoiding them rhetorically with

recommendations but by highlighting the non-

strategic nature of unethical approaches that are

only effective in the short term and by creating

situations in which the unethical nature of such

approaches is not convenient in the long term.

The interest in studying manipulative

approaches to social systems lies in recognizing

them and in implementing appropriately

neutralizing approaches.

4 Conclusions

In this work, we introduced a focus on the unusual

issue of de-emergence, intended as the deactivation

of processes and properties of togetherness, such as

emergence and self-organization. We mentioned

related possible theoretical issues that should be

explored and extended. This is about the theme of

reverse emergence, which is understood both as the

effects of the emergence process on the

phenomenon from which it emerges and as a study

of its dismantling. We considered two cases, i.e., the

Rayleigh–Bénard convection and social systems,

and some possible tentative approaches for their

deactivations. This is to provide an idea of the

admissibility of the problem and the possible

approaches. It is, therefore, a research project open

to new approaches and modeling.

However, the theoretical approach consists of

progressively eliminating the conditions necessary

for the establishment and maintenance of emergence

processes (such as equivalences, high levels of

degrees of freedom, and incompleteness mentioned

above) or in any case correlation relations

generating coherence. This is an approach with a

conceptually different nature, not multiplicative, for

example, from those considering an increase in

propagation, diffusion, for example, epidemic, and

positive feedback, admitting gradual and partial

reductions.

Moreover, let us consider at this point the

difference between first-order phase transitions that

admit coexistence of phases (for example, the

boiling of water in which the water does not

instantly transform into vapor, but there is

coexistence between the liquid and vapor phases)

and second-order phase transitions that do not admit

coexistence between phases (for example between

ferromagnetic and non-ferromagnetic). Since

emergence is intended as constituted of coherent

sequences of multiple, local, and temporary self-

organization-like collective phenomena that admit

coexistence, it may be intended to have a first-order

phase transition-like nature.

It could therefore be assumed that the nature of

approaches used to deactivate first-order phase

transitions may be appropriately considered for the

deactivation of emergence processes. However,

coexistence in first-order phase transition processes

should be considered replaced by compatibility and

coherence.

Furthermore, it is a matter of developing

approaches capable of detecting the establishment,

initial conditions for the establishment of collective

phenomena of emergence, of coherence.

A different approach is based on acting on

parametric values considering the availability of

approaches for measuring, [59], [60], [61], levels of

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coherence and emergence also capable of detecting

initial constitutive phases.

Memory note:

This paper is dedicated to the memory of Professor

Hermann Haken.

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