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This paper combines the complexity notions of phase transitions and tipping points with recent advances in cognitive neuroscience to propose a general theory of human proto-organizing. It takes as a premise that a necessary prerequisite for organizing, or “proto-organizing,” occurs through emotional contagion in subpopulations of human interaction dynamics in complex ecosystems. Emotional contagion is posited to engender emotional understanding and identification with others, a social process that acts as a mechanism that enables (or precludes) cooperative responses to opportunities and risks. Propositions are offered and further research is suggested.
There has been an increasing call for approaches that might be used to build theoretical microfoundations for social theory (
This article contributes a new way of thinking by taking a contrarian approach. We argue that the drive to cooperate within human populations may have preceded, in an evolutionary biology sense, the complex rational cognitive processing that are assumed to enact decision making. In the proposed model, we suggest that the dynamics of human organizing might be a primordial aspect of human society that derives from emotional cognitive systems and a phenomenon known as
This emotional contagion may lead to a shared community-identity about how one’s feelings about the situation (rather than what one thinks about it) might impact the choices and actions that might need to be taken. Because everyone feels the same way, each individual can better anticipate how others will respond to events. Their “action vectors,” as one might call them, can be observed to align toward a common direction of action. Under these conditions, the subpopulation is primed to act “as one.” We call this process “proto-organizing” to distinguish it from rational organizing that is orchestrated under a clearly articulated collective purpose. When proto-organizing remains stable over time, even when some individuals leave and others join the population, we call this stable dynamic pattern “proto-community.”
In the first section of this article, we contend that individuals tend to be in one or the other of two distinct emotional states: a positive emotional attractor (PEA;
In the third section, we contend that when certain parametric conditions develop in the ecosystem (e.g., a newly perceived opportunity or risk) one of two states can become “contagious” (
The theory we propose consists of multiple elements. The first of these is that individuals can be modeled as heterogeneous
In contrast, PEA is a state that is defined by the human or human system aroused in the parasympathetic nervous system (PNS), feeling positive affect, and activation of the default mode network (DMN;
In complexity terms,
Tipping points act like the peak of a mountain ridge separating two adjoining valleys one to retain each stable condition, NEA or PEA as a personal disposition. If a perturbation, whether strong or mild, exceeds a “threshold” value, it can push someone who had been in a PEA state beyond the ridge or “tipping point” that separates the two attractors. The neurological, hormonal, and affective system can “flip” to the other state transitioning the individual into an NEA state. Continuing the mountain ridge metaphor, one’s emotional state “falls” into the “NEA attractor valley” beyond the ridge separating the two states.
The tipping points moving from one attractor within the neurological dynamical states inside the individual to another—either PEA → NEA, or NEA → PEA—is assumed to occur close to the 0 point of these three axes (
Negative events (over the threshold) for individuals can trigger their defensive, analytical NEA state. Threats must be analyzed; risk mitigated. This state may also strengthen their tendency to rely on basic natural categories (
In contrast, a positive event (over a threshold) can trigger the PEA state which promotes openness and willingness to explore possibilities, where the individual relies more on intuition and confidence. This state may lessen one’s tendency to rely on basic natural categories (
In a later section we describe how collective emotional states might be described through models of social contagion. For example, one would expect that when NEA becomes dominant, individuals experience stresses from their ecosystem together as a group. When individuals survive, they do so together. If in the process, they build trusting relationships which they can count on for useful information including emotional information, the relationships themselves may become a basis for a tipping point into the PEA as they share and help each other. The experience of gratitude and compassion are key emotions that can tip a person into the PEA.
In dense social environments (beyond a certain threshold density level), either of these emotional states can quickly be transferred from some individuals to others through interactions and the feedback effects of emotional and social contagion, the process of transition from “me” to “we” that is discussed in a later section. Before discussing this, however, we first describe the baseline assumptions that we posit as a foundation for a formal model.
Absent exogenous constraining forces or events in the ecosystem and assuming a resource rich environment with little predation or competition, a population of individuals within the ecosystem might be assumed to form a sparsely connected population of largely autonomous individuals or affinity groups. Each of these individuals would adopt one or the other of the two emotional states based upon internal physiological and neurological dynamics as influenced by individual traits, locally relevant conditions, and the quality of their relationships. For example, family members might influence one another, but when viewed from the perspective of a large enough population, these localized effects would tend to cancel each other out. Ignoring these local effects, one might measure the aggregate emotional state at the population level with a macroeconomic indicator such as the Conference Board’s consumer confidence index®, or on the negative side, as a measure of escalating obesity, average blood pressure, or feelings of anxiety or distress.
In this simple baseline case that we call the
The emotional state of others is locally observable (and increasingly so with social media), however, and these observations contain information to be recognized and gathered by others. As a result, in the more general cases discussed in later sections, information gathered about the emotional states of others might be useful to individuals—among animals, a deer might flash the white underside of its tail as a warning, for example, or a beaver might slap the water with its tail. Information about the emotional state of others thus becomes an additional factor influencing the emotional state of any particular individual. When synchronized patterns associated with the flow or
To begin, we define the substrate for this process by taking the simplifying assumption and treat PEA and NEA attractor states in individuals as representing two fixed point attractor states that characterize the two possible states for each individual within the population. This means that in the population model, individuals are treated as being in either the PEA state or the NEA state, regardless of the internal dynamics and neurological complexity within each individual’s brain. For simplicity, transitional states and the subjective experience of changing ones emotional position are treated as noise from the population perspective.
Each individual is assumed to be in or shift between two dynamically stable emotional states as individuals go through internal transitions in emotional state (a process that is beyond the scope of this paper). For a population of N individuals, the state,
For illustrative simplicity, we assume that the mean for the population is normalized to 0—that is,
As population density increases and individuals begin to interact in close-quarters, it becomes necessary to relax the assumption that an individual’s emotional state is independent of his or her neighbors. Research in social psychology has shown that emotional attractor states can be contagious and transmitted from one individual to another, spreading like an infection and thus potentially modeled using epidemiology models (
Emotional and social contagion depends, of course, not only on the ability of other individuals to correctly recognize the signal of an emotional state through body positioning, facial expressions, or verbal cues (cf.
More precisely, in cases of population density above a certain threshold, this amended assumption with regards to interaction affects can be stated as follows: under certain conditions, interactions between individuals can trigger phase transitions in interacting pairs or interacting groups (cf.
When subpopulations within a broader population become inter-correlated, information about emotional states is transported between neighboring individuals. Through this social process, information about the ecosystem that is observed from different perspectives by a few individuals can be “stored” in the population’s organizing structure for retrieval and use by others. It is stored as ordering or symmetries within the correlated emotional states that “solidify” within the subpopulation. Because this signal would not exist (or would be very improbable) absent particularized disturbances in the environment, the stored information can be recognized and used by other individuals. This implies that information about the ecosystem that is stored in structure through emotional contagion can be observed and decoded by individuals to inform their behaviors even absent their own direct observation of events. Over time these regularities can become stable, reflecting a form of proto-organizing that enables efficacious action by a collective. This is akin to
The dynamic process of human proto-organizing is an example of emergence (
The process of emotional contagion is therefore fundamentally about information transit: information about opportunities or risks flows through the population, albeit without words for the most part, but rather mood. Note that the use of a previously developed symbolic language is not a prerequisite for this contagion. Although information is being transferred from person to person, this is not accomplished through symbolic language
In the next section we describe parameters that describe the conditions that might lead to this capacity to recognize and respond to an external event in the ecosystem. Specifically in the model we are proposing, we adapt the model of
This section proposes that emotional states and how they spread through the population are an observable biological mechanism that reflects conditions in the environment. In particular, this paper posits that the
the presence of a significant disturbance in the external resource flows that may have complex impacts on individuals or groups who are at that point in the ecosystem, and
the fidelity and complexity of the transit network for information about the disturbance that flows internally among individuals in the population.
This latter parameter relates to the density of social networks and the longevity of connections (and thus their trustworthiness) within the population. It might also relate to other factors such as ethnic and cultural partitions in society.
Following the model of social innovation of
The first parameter measures
The second parameter
The first parameter,
This parameter reflects how the changing potential for acquiring and using needed resources impacts the interactions among individuals in the population when they are treated as autonomous actors. When a potential opportunity or risk presents itself, each individual implicitly asks in the context of an emotional reaction: Do I (or we) prosper? Do I (or we) compete? Must I (or we) cooperate to maintain access to the resources? These questions create cognitive and emotional tensions among individuals struggling to answer them. As an example, consider how groups cooperate to build, maintain and defend a dam and reservoir in order to maintain a constant water supply in support of a safe community. Biologists who argue for group evolutionary selection processes call these collective dynamics “nesting safety” in support of a “defensible nest” (
A simple (low complexity) event might be an unambiguous disturbance such as a fire in the theater. The appropriate NEA versus PEA is immediately clear to others when someone yells, “FIRE!” People do not spend much energy looking to others to confirm the danger. They immediately adopt an emotional state (most probably NEA) and take action to protect themselves and their dependent loved ones. Because this is a simple, unambiguous signal that does not require collaboration to decide on which action to take, except perhaps locally among families, an emotional state is adopted quickly with a high probability and with a single emotional interaction, or “dose.” Because the threat is apparent and clear and although there might be fear and anxiety, there is little internal tension; the transition to a dominant emotional state is continuous, and in this example, probably quite fast. In this simple case we say the value of the opportunity/risk tension parameter is less than 0, that is,
On the other hand, a disturbance might be less transparent and be difficult to interpret, for example changing weather patterns. In such cases, disturbances in the environment might be reflected as internal emotional disturbances within the community such as heated discussions and disagreements and the resulting emotional tension among those affected. Rituals like screaming matches, stylized displays of power, appeals to superstition, metaphoric “rain dances” or sacrifices might be used to evoke desired emotional reactions in others in an effort to synchronize emotional states.
A powerful competitor’s aggressive move into one’s market might require a response, but the specific response might be unclear. Does the organization abandon its market and move to a different one, cordon off a niche market and prepare to defend it, or to take some strong competitive initiative (perhaps legal action) to preserve access to its markets or other resources? The attack is a disturbance which causes an emotional response within the organization. However, in this case, the need to formulate a coordinated collective reaction creates internal tension in the organization as the collective’s emotional response only gradually becomes synchronized (more slowly than in the theater fire example). This class of disturbance requires multiple “doses” of emotional interaction to synchronize with others. As a result, there is usually a time delay. Under these complex conditions the opportunity/risk tension parameter is greater than 0, that is,
There is a point along this opportunity/risk continuum between simple conditions and more complex conditions is called the bifurcation point. This point distinguishes between conditions where disturbances are so transparent that individuals easily assume an emotional state with a single event or “dose” and those situations where disturbances are ambiguous, potentially impacting both the individual and the larger collective upon which each person depends, perhaps in complex ways. In this latter case, the individual seeks interactions with others to determine how one should respond. The bifurcation point is where
We argue that proto-organizing begins beyond the bifurcations point, where
In the aggregate, the opportunity/risk tension parameter measures how an aggregate in the population reacts to resource and risk conditions within its ecosystem. Using the venture capital ecosystem as an example, the opportunity/risk tension parameter might measure the flow of funds from limited partners into various types of venture funds and the flow of these funds into ventures at various stages of development. The availability of seed or early stage funding opportunities versus late stage financing might create different levels of tension among venture capital funding transactions. In cases where opportunities require small investments relative to the size of the fund and opportunities are ubiquitous, like SmartPhone applications soon after the Apple iPhone was introduced, seed and start-up funding is likewise ubiquitous. A single investor or perhaps an angel investor might quickly become excited into a PEA state and independently fund the venture. This would be cases where
More often, however, are cases where
A fecund ecosystem can also be marked by competition. In these cases, events can signal a level
When there is little ambiguity along the opportunity/risk dimension, when
As the opportunity/risk tension parameter increases beyond the bifurcation point, that is where
If one assumes 0 fidelity in the information transfer about emotional states among individual agents (i.e., they are members of what might be called different clans or enemy tribes), then individuals experience and exert no mutual influence. In these cases, knowing the state of one individual does not directly predict that of its neighbors, and there is no local correlation (other than close-in groups like families) except with regards the central tendency statistics
The second parameter,
This parameter reflects the extent to which the informational differences about emotional states present in the ecosystem transmit relevant signals accurately (with fidelity) throughout the subpopulations as they form proto-community identity. For example, is a warning signal that is issued by a particular individual trusted and heeded by others or is it ignored as a “boy crying wolf” as in the fairy tale? Does that individual’s emotional state spread to others and if so at what “infection” rate? Fluctuations like these can be seen in financial markets where information about exogenous macro-events is interpreted by traders within the markets and perceived opportunities or risks spread through the population of professional and then amateur investors through contagion. The density and connectedness, especially trusted connectedness of networks, are factors influencing this parameter. In business organizations, a possible surrogate metric at the firm level-of-analysis might be what
The emergence of a proto-community through these dynamics is evident in most sustainable community development efforts and missing in those that do not survive (
The value of this parameter reflects the level to which there is an opportunity for the individual to “synchronize” his or her emotional state with that of others who share a community-identity. By doing so, individuals can align emotionally to take advantage of an opportunity to gain resources or escape a threat that might impact the community. We argue that this “proto-organizing” only occurs in the interval between two extreme cases. For the case where the social network is sparse and weak with little trust and affinity, where
Self-sacrifice in the case of high proto-community potential is in contrast to situations where the presence of informational differences among emotional states is closely held and not shared with others due to competition, or cases where signaling might actually be deceptive (e.g., as feigned madness, misdirection, or outright lying) to gain advantage in the struggle to position oneself to acquire and maintain access to resources and minimize risk. This latter case implies a possible imperative to “defect” in game theory terms and to not synchronize with others, influenced perhaps by a sense that the emotional information within the ecosystem is not reliable (
To summarize, the proto-community potential parameter represents the potency of information gathering and use (
This article posits that the reliability of the transit of emotional state information reflects several elements of the underlying or substrate social network, including for example the density and trustworthiness of connections within the population (see Figure
This section proposes an
An order parameter that reflects the presence of proto-organizing is the final factor needed to specify a mathematical relationship that might be used to describe the potential for individuals to form into a system of proto-organizing in the aggregate at a point in an ecosystem. A group of previously autonomous individuals might be organizing into a meeting, for example, and this might be observed as the stabilization of the order parameter. More specifically, under certain parametric conditions, the order parameter would identify the potential for discontinuous phase transitions between two dynamically stable emotional attractor states within aggregates of individuals in a population: One state would be ordered but stable in a certain way, positive and cooperative, for example. The other might be ordered and stable in a different way, negative and combative, in this example. The dominant emotional state of a meeting (that is, the aggregate is the set of participants in a meeting) that is being observed, could potentially change quite suddenly between states, for example, a property that cannot be reduced to the individual level.
The transition from one state or “phase” to another is thus measured by the order parameter. We define this order parameter, ξ, such that it is measured as points along the interval [–1, 1], with –1 reflecting a population with a 100% in one phase orientation, the NEA, and the value of 1 reflecting a population with a 100% in the other phase orientation, the PEA. Under this definition, a phase transition occurs when the order parameter changes from nearly –1 to nearly 1 or from nearly 1 to nearly –1 and remains dynamically stable for a time.
We propose that the potential for an aggregate to proto-organize can be measured by this order parameter. Further, we argue that the value of this potential, ξ, can be modeled as dependent upon the interacting values of the two control parameters discussed previously. The opportunity/risk tension parameter,
To recognize a phase transition, we would be looking for an order parameter that measures the proportion of the population that vectors toward cooperative action, which we posit is the PEA state in the context of a complex and thus initially ambiguous resource environment (i.e., the “we” or “one” state, as relates to a specific shared identity) versus the proportion of the population that vectors toward defensive self-protective action, which we posit is the NEA state (i.e., the self-interested “I” or “me” state). This proportion would be measured as points along the interval [–1, 1], with –1 reflecting a 100% “me” orientation (individualized activity posited to be the NEA state), and 1 reflecting a population with a 100% “we” orientation (cooperative activity posited to be the PEA state). The order parameter ξ does exactly this.
Under this definition, a phase transition occurs with respect to individual alignment with regards a specific condition when the order parameter changes from a stable “me” dominated aggregate state to a stable “we” dominated aggregate state, or from “we” to “me” in a particular context. When there is opportunity/risk tension beyond the bifurcation threshold as shown in Figure
Thus, we define the
Later, we will show that this relationship is the cusp of change model described by
It may be the case that proto-organizing into a stable PEA state of an aggregate within a population is itself recognized by individuals as an event with potential implications for the individual. Recognizing this type of event can itself elicit a PEA or an NEA state in individuals. It is reasonable to assume that a positive feedback process might ensue such that additional individuals synchronize with the stable PEA state. What we are calling a
Under these conditions, the proto-community’s shared PEA state supports the norm of cooperation with others who share the same community-identity through a reinforcing feedback loop as follows. When an individual’s community-identity is activated by events or conditions in the ecosystem, that individual’s PEA emotional state is likewise activated. This activation influences others in the community to synchronize their emotional state, and when they assume the PEA state, the first individual is positively reinforced in the PEA state, and so on, in an activation process much like a neural network (
Certain conditions can cause one or the other of these two emotional states, PEA and NEA, to be “contagious,” like an infection that spreads through the population. There are conditions and times when there is a disturbance in the environment that is detected by some individuals but potentially not others (like Obi-Wan Kenobi or Yoda sensing a disturbance in the force), some sort of opportunity or risk that is in excess of a measurable threshold, whereby the population takes notice, such as a coming financial crisis or a shortage in the oil or water reserves
The challenge for research is to identify and measure the conditions in the ecosystem under which the population is stirred through emotional contagion to respond
To illustrate this idea, emotional contagion and its relationship to coordinated action could be observed in the response of financial policy makers around the world after the fall of Lehman Brothers in September 2008 (
As we describe in this and the next main section, emotional contagion occurs when a person in either the PEA or NEA state begins influencing others to synchronize with them into the same state. This transfer of state through interaction is referred to as the transit of information (about their respective emotional state). Physiologically, through the mechanisms of mirror neuron networks (
As a result of the transit of information, the activation of a synchronized emotional state within a population can occur unconsciously and quickly, and thus a common state can potentially spread across the entire population. A sudden change in the aggregate state—as measured for example by the normalized consumer confidence index®—is called a
In particular, as is described in the next main section (also see
A second factor would relate to the strength of the warning offered during the interaction. A subtle, ambiguous, or indirect emotional expression related to a resource opportunity or risk is quite different than an impassioned argument in favor of community-level mobilization to action among connected others. In infectious disease term, what is the dosage level?
Beyond this, a third factor is how broadly and consistently the story is told. Does the informed party treat everyone the same, with the same impassioned warning, or is the emotional display more selective? Does one only feel free to display emotion openly in a close-in group, or more broadly? What is the distribution of dosage size? Finally, the uninformed must recognize the message (that is, an emotional display indicating fear, for example) and react, changing state to synchronize with the informed voice. How do people react? Does everyone react the same way? Are they different? What is the distribution in the population? All of these questions must be explored when modeling proto-organizing and the conditions which engender the formation of proto-community identities.
To explore this question, we will examine the generalized model of contagion developed by
To inform our discussion, it is useful to point out that
By comparing the probability
The analysis of
On the other hand, if the probability of adopting the emotional state of the other on the first interaction
There are many empirical questions that are embedded in this argument. For example, the nature of the PEA state is more neurologically open to new ideas, and this may result in people who are in a PEA state being slightly more vulnerable to contagion. On the other hand, the defensive and ecologically imperative nature of the NEA as a basis for survival may result in people having a default condition of reverting to the NEA, rendering them more vulnerable to infection or contagion from others in the NEA state. The documented evidence claiming that negative emotions are stronger than positive (
In this section we develop a simple model of a phase transition in emotional states from a “me” orientation to a “we” orientation in human interaction dynamics (HID). To explore this dynamic, we build upon the cusp of change model described by
We assume that for a given subpopulation, emotional contagion occurs when the order parameter for a subpopulation changes significantly and becomes stable at a new level, an event that signals to observers that proto-organizing is occurring. The signal is observable because one can recognize that the locally synchronized emotional states within the subpopulation have become differentiated from the background and stabilize at that differentiated level. As this occurs, individual emotional states spread through the subpopulation as individuals form a shared community-identity of “us” and “we” that is rooted in emotional synchrony and establishes the subpopulation as persistently different than the background at least in this one aspect. This is a proto-community.
For simplicity, we initially assume from the
In cases identified by
This local dynamic implies that phase transitions in the broader population can occur if the HID are such that proto-communities in subpopulations also synchronize with one another across the entire population. These interaction dynamics are assumed to relate to social or emotional contagion processes internal to the population, and these may not be homogeneous. For these cases,
However, to advance this thinking initially, we propose the Cusp of Change Model (
To recapitulate, emotional contagion is the process where positive or negative emotional states spread through and synchronize within a population via emotional and social interaction. It can occur gradually in response to changing opportunity/risk conditions or it can be sudden, a phase transition. The latter case is enabled when two factors interact to create the requisite enabling conditions as shown in Figure
The parameter
The parameter
Under these parametric conditions, the process unfolds as follows: a subset of individuals who find themselves in a position to recognize the opportunity or risk directly in the environment assume varying emotional states, consciously or unconsciously. Through interactions with others, these emotional states can spread to others who did not directly observe the event. The likelihood of “infection” depends upon each individual’s susceptibility to socio-emotional influence from a particular interaction including relative status (
A. the level of emotional display by the ego, the “dosage,”
total dosage accumulated within the alter from recent prior interactions,
the number of recent interactions that are accumulated by the alter in “memory,” and
a level that exceeds the alter threshold beyond which an accumulated dosage implies synchronization, i.e., an infection, of emotional state.
In this way, even subtle and complex forms of organizing behavior can be modeled, providing powerful methods for better understanding human social and emotional organizing potentials and outcomes—the goal of HID as described by
Note, however, that emotional contagion alone does not unite individuals into a cooperative effort. Rather it just gets people into similar emotional states, either open to influence from others (PEA) or suspicious of it (NEA). We also suspect that because negative emotions are stronger than positive ones (
We have presented a theoretical and mathematical approach for describing the formation of proto-communities as a first step to social organization. This proto-organizing occurs through the process of emotional contagion. The paper includes propositions intended to guide future research.
The theory identifies the drivers and mechanisms that describe how synchronized emotional states emerge. This occurs through what amounts to a swarming process in insects and is related to infectious disease and social contagion dynamics that have long been studied and modeled in epidemiology. The point of departure here is the argument that human organizing itself is enabled by changing emotional states rather than rational choice.
Our premise is that when emotional states are synchronized through emotional contagion, a proto-organizing state emerges within the population, and this is the mechanism that enables coordinated action, including rational planning activities and the implementation of action plans. Further, the emergence of an emotionally enabled proto-organizing state precedes rational choice (perhaps only momentarily) and is a necessary precondition to the development of a rationally understood organizing structure.
Thus, this paper contributes not only new theory, but also a whole new perspective on organization theory. It proposes a framework which elevates the utility of emotional experience in organizations to an equal level with the rational-centric perspective that is usually the implicit orientation of management research. It makes the bold argument that organizing begins with and is enabled by emotional processes rather than rational ones. Emotions come first, and rational experience augments emotional experience rather than the other way around. The inverted perspective identified here, if supported empirically, would make it clear that the effective navigation of the emotional landscape in unfolding organizations is an essential skill for managers and leaders. It is central to success at all levels, and as such it deserves adequate focus. This is in contrast to the vast majority of the management literature where emotional experiences are considered an annoyance at best, are ignored as irrelevant most of the time, or in some cases they are even highlighted as a dangerous distraction to be avoided by skilled practitioners.
Recent advances in neuroscience, contagion modeling, and the complexity notion of swarming offer an opportunity to change this bias. They imply that it is time for organization and management research to better explore the organic nature of human organizing in ways that include both
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
As proto-organizing structures unfold across the organization, influence is not homogeneous. Individuals vary in status for example (
Note that information about a disturbance in the ecosystem that is recognized by an individual might benefit others or it might be used against them. Deception in organizations might be an interesting area of research in the emotional contagion context.
For example the mean might be 50% PEA or it might be 70% PEA, and this would predict, but knowing what the neighbors are does not predict.
As pointed out by one of our reviewers, there is an important philosophic question here as to whether such changes can also be initiated endogenously, or whether there is always an external cause. This question is caught up in how one defines the system, of course. For this analysis, we take the relatively hard position that the cause is always external from the position of the system under study.
More specifically, in Class I, where