Navigation – Plan du site
Varia

Asygnosis and asygnotic networks

A theory of emergent unconscious knowledge
Edward M. Roche et Michael J. Blaine
p. 419-450

Résumés

Le but de cet article est de créer un dialogue entre les disciplines disparates autour des thèmes variés : prise de décision, réseaux de communication humaine, échanges d’informations. L’émergence des télécommunications modernes, y compris les médias sociaux, les plateformes de jeux, et la messagerie instantanée a permis l’émergence de communautés mondiales en ligne, communautés réunissant des dizaines et des centaines de millions de personnes capables d’échanger des idées, des images et des impressions en temps réel. Cela indépendamment de leurs cultures et langues. L’ampleur sans précédent et l’échelle de ces réseaux défient les théories contemporaines en gestion, prise de décision, organisation politique, gouvernance des institutions, sociologie et communications. Nous soutenons que ces théories sont dépassées et doivent être repensées. Nous proposons un modèle simple, dit "asygnosis", dans lequel les décisions et les stratégies sont le produit du flux continu (non discret) d’informations, d’idées et d’impressions (« mèmes ») sur des réseaux de communication entre des individus et / ou des machines. Ces réseaux reflètent l’émergence d’une connaissance autour d’une idée ou une activité spécifique. Ils remettent en cause les hypothèses traditionnelles sur la rationalité, la causalité et l’utilisation des structures organisationnelles ; ils remettent en cause aussi les mécanismes de contrôle qui constituent la base de la plupart des théories sociales, économiques et politiques. Après avoir développé le concept de réseaux asyngnosis et asyngnotic, nous l’appliquons à des questions d’actualité, y compris des événements imprévisibles qui se déroule sans contrôle direct, comme la montée du terrorisme spontané, et d’autres phénomènes sociaux et politiques tels que le Printemps arabe et ’Occupy Wall Street’. Enfin, le document suggère des domaines de la recherche future dans un certain nombre de disciplines qui pourraient aider à développer le concept plus loin, les hypothèses de test, et conduire à de nouvelles connaissances sur ce phénomène émergent.

Haut de page

Texte intégral

Introduction

1The purpose of this paper is to develop a new framework to explain a number of seemingly unrelated Organizational, Social and Political phenomena all of which revolve around the sharing, processing and use of “information”, images and concepts (“memes”). Our framework began to emerge several years ago when an historical review of Management Theory made it increasingly apparent that something new was happening in the Decision-making and Strategy processes of major organizations. Existing theories in Economics, Organizational Behavior (OB) and Strategic Management (SM) failed to accurately describe or explain many important Organizational and Behavioral phenomena, and recent research in these and other fields is a tacit acknowledgement of this fact. But while Network Theory (Jones, Hesterly, Borgatti, 1997 ; Reagans, Mc,lEvily, 2003 ; Tsai, Ghoshal, 1998), Emergent Strategy (Doz, 1996 ; Mintzberg, 1987), Behavioral Economics (Donaldson, Preston, 1995), Neuro-economics (Montague, 2007 ; Volk, Kohler, 2012 ; Weisberg, et al., 2008) and other new streams of research have played an important role in expanding established theories and redressing some of their shortcomings, this research does not go far enough in explaining and predicting important developments.

2Our initial approach was to reassess several of the most basic assumptions of Economic and Management theory in light of contemporary research in other disciplines. As new knowledge begins to challenge the often implicit, core assumptions of established theories, the theories themselves are brought into question. Thus, by tracing the logical and historical interconnections between key assumptions we can identify concepts that need revision and/or extension and take a useful step in developing new, more powerful theories that better reflect current understanding. We examined three key assumptions that provide the foundation for much Economic and Business theory, particularly Decision Science (DS). The first, “rationality”, seeks to explain individual behavior and the nature of the decision-making process. The second, “causality”, concerns the nature of reality and the ability of actors to effect outcomes. The third involves the use of basic organizational structures and processes as control mechanisms. Our argument is that these assumptions no longer reflect contemporary understanding of either human behavior or the nature of reality and must be replaced by more appropriate concepts. But more importantly, by suggesting that the basic assumptions and models of DS are no longer valid, we are also bringing into question many of the most basic models and theories of SM and OB.

3This paper proposes a new concept we call “Asygnosis”. It grows out of recent research in Physics (Goswami, Reed, Goswami, 1993; Stanley, 2014), Biology (Back, 1971; Penrose, 1952), Neuroscience (Powell, 2011; Senior, Lee, Butler, 2011), and Experimental Psychology and Economics (Laibson, Zeckhauser, 1998; Livet, 2010; Siakantaris, 2000; Tammi, 2003). Although the concept was originally designed to explain Decision-making (and Strategy creation) in organizations, as we will see, it is also useful in studying certain Social and Political phenomena, particularly the emergence and growth of various types of communication networks (“Asygnodic” Networks) based on new technologies and Social Media. Further, since contemporary models of Decision-making have historically provided a template for models of Strategy development and Organizational structures and processes, Asygnosis should be useful in a number of other related contexts.

4The paper is divided into a number of sections excluding this brief introduction. The first section reexamines three core assumptions of SM and OB, and highlights the linkages between DS and the basic Strategy Process. This is followed by a review of relevant research in other disciplines which poses serious challenges to the core assumptions noted above. Using an alternative set of assumptions which better reflects contemporary knowledge in Physics, Neuroscience, and Behavioral Economics, the third section develops a new “model” of Communication and Information Processing which we call “Asygnosis”. We use it to explain many anomalies in the Decision-making and Strategy processes, and discuss its implication for SM and OB. The next section examines the emergence of “Asygnodic Networks” (ANs), giving an overview of their lifecycle and impact, and providing recent examples of these new structures. The final section explores some of the implications of our model, and suggests a number of possible directions for future research.

Decision-making and the strategy process

5The need to make highly complex decisions with information of unknown quality and with few or no precedents, often in a short period of time, is a common problem facing strategists and decision-makers in large organizations such as the multinational enterprise (MNE) (Egelhoff, 1991; Kriger, Solomon, 1992; Rosenzweig, Singh, 1991). As a result, theories and models of decision-making have historically played an important role in the development of theories in other disciplines, particularly SM and OB (Cyert, Williams, 1993 ; Eisenhardt, Abaracki, 1992 ; Kaplan, 2008, Kumar, 2014), and have also been the basis for critical organizational processes such as strategy formulation and implementation (Garvin, 2012 ; Wrona, Ladwig, Gunnesch, 2013).

6Modern theories of decision-making originated in the early 20th century with Weber (1922) who emphasized hierarchical organization in which authority and basic structural processes led to relatively predictable decisions based on a universal set of rules that were free from individual discretion. Weber believed in “rationality” in the sense that decision-makers weighed the consequences associated with different courses of action and acted in the most beneficial manner (i.e., to maximize outcomes). He also implicitly accepted the concept of “causality”, meaning simply that individual and group decisions and actions have the power to effect subsequent events and outcomes. And of course, the idea that simple structures and processes could be used to control and manage organizations became fundamental to classical OT (Taylor, 1914; Chandler, 1990). As SM emerged as an independent discipline in the 1950s, it drew heavily on contemporary research in OB and DS, and as a result, the underlying assumptions of that research became deeply embedded into the new discipline.

7It is interesting to note, for example, the similarities between early models of decision-making and the basic strategy process (SP). Symonds (1936) identifies 5 key steps in “classical” decision-making : (1) identifying the payoffs (“values”) associated with a given decision ; (2) evaluating alternative decision options ; (3) gathering information about the environment and possible (future) events ; (4) estimating the consequences of alternative events ; (5) weighing the relative payoffs of a decision option given the probability of certain (future) events. The option with the highest payoff given the most likely future state is the decision taken. With slight variations, this model also provides a template for strategy formulation and implementation (Drazin, Howard, 1984; Johnson, Wallace, 2009; Lorange, 1998). The SP begins with: (1) the definition of a clear set of objectives. This is followed by: (2) a search of the internal (organizational) and external (competitive) environment in order to identify key threats and opportunities and assess organizational resources and capabilities (Barney, 1991; Porter, 2011; Priem, Butler, 2001). Given this information: (3) a specific strategy is developed, followed by: (4) the process of implementation, which frequently requires changes in basic organizational structures and processes (Alexander, 1985; Roth, Schweiger, Morrison, 1991).

8This “structural” approach to decision-making and strategy formulation worked reasonably well until growing organizational and environmental complexity, and the need for rapid response and action overwhelmed primitive bureaucratic systems, leading to breakdowns in the reporting chain and unacceptable delays. Nonetheless, this simple model is still widely used, and is the basis for many organizational processes designed to ensure that critical strategic decisions are made in a prescribed manner (Ouchi, 1981 ; Bourgeois, 1981), without “distortions” (Mintzberg, 1989) and without regard to the individual “style” of the decision-maker (Eisenhardt, 1989).

9Since the 1960s, Mathematics (especially Probability Analysis, Game Theory and Systems Theory) and Computer Science have become major influences on both DS and SM, and a plethora of increasingly sophisticated models and methods have emerged to evaluate the external environment, forecast possible future events, and guide decision-making (Daft, Weick, 1984). This research has had several important effects. The most obvious has been to break strategic planning and decision-making into increasingly narrow activity segments which are then complemented by highly complex organizational processes and computer-based tools (Nunamaker, Applegate, Konsynski, 1988). These new Decision Support Systems (DSS) have allowed the SP to become virtually continuous by allowing the constant collection, processing and analysis of critical data; but they have also led to the “standardization” of critical organizational processes which restricts individual behavior and creativity. Further, with the rise of enterprise-wide information systems in 1980s and 1990s, the cost, time and complexity of building and managing these structures has become a major impediment to organizational innovation and change (Scott, Vessey, 2002; Umble, Haft, Umble, 2003). Thus, while these efforts may foster Organizational Knowledge and Learning (Alavi, Leidner, 2001; Nonaka, 1994), they have also come at a cost. From our perspective, however, the most important impact of these advances has been to embed the core assumptions of rationality, causality, and organizational control so deeply into SM theory and practice that they are rarely noted, let alone questioned.

The challenge of new research

Rationality

10Challenges to the core assumptions of most Economic and Decision Theory come from many sources. By the late 1950s contemporary research had already raised questions about the ability of large, complex organizations to make anything like “rational” decisions. One stream of research has focused on the inability or unwillingness of individuals to act rationally (Cyert & March, 1963, Simon 1964). For example, researchers found that the psychological state of the decision-maker influences decisions (Siegel, 1957), as well as the subjective assessment of risk and uncertainty (Beach, 1975). In addition, the importance of a decision often drove a search for more information (Lanzetta & Driscoll, 1969), which increased complexity and made it difficult to act rationally ; while inadequate information, uncertainty and ambiguity inhibited decision making (March, et al., 1979), by encouraging the adoption of familiar, but not necessarily optimal, solutions (Pfeffer et al., 1976). It is also widely known that decision makers have individual “structural styles”, psychologies, ideologies and mind-sets (Fiol & Lyles, 1985) that vary from one situation to the next. In short, every individual has biases or misperceptions, “mental models” (Converse, 1993), behavior (Greenberg & Baron, 2008; Hall & Tolbert, 2009) or emotions and self-interests, all of which work against the strict “rational” theorized by Weber.

11A second stream of research focused on problems inherent in the decision models themselves, and found that even the most “rational” decisions often turned out to be sub-optimal (Aldrich & Pfeffer, 1976) due to inaccurate assessments of future events and/or the outcomes associated with a given action (bounded rationality) (March, 1978). It was initially thought that many of these problems could be eliminated by integrating advanced Information Technology (IT) with more sophisticated decision models based on Probability Analysis and Game Theory. These Man-Machine systems eventually lead to IT-enabled groups that some argued produced more rational outcomes (Desanctis & Gallupe, 1987), but continued to exhibit identifiable biases. In addition, the models and tool used to support the decision process eventually became too complex for the average worker to use effectively (Brehmer, 1992). Thus, although IT allowed decision-making to become more diffused (Huber, 1990), less hierarchical, and more decentralized (Gurbaxani & Whang, 1991), at the core of the process there was still an individual who did not behave “rationally”.

12Recent research in Neuro-Science and Experimental Economics (Maital, 2004) provides useful insights into this apparent “non-rationality”. Based on current understanding of the structure and function of the brain, decision-makers have much less “free will” than previously thought, and many events that we call “decisions” are neither logical nor rational but simply reflexes or emotional responses. Further, decisions-making does not appear to take place sequentially and is not located in a specific part of the brain. Instead it occurs through networks of simultaneous “neuro” events that take place throughout the brain (Rilling, Sanfey, 2011; Levine, 2009; Jarrell, et al., 2012). Consequently, models and DSS that structure the decision process sequentially, exclude unrelated or unnecessary information and limit access to other individuals may actually inhibit - rather than support - effective reasoning and judgement. Finally, there is growing evidence that the constant interaction with advanced technologies - from video games, to computers and cell phones - may actually cause permanent changes in brain activity and function. Thus, it seems increasingly unlikely that the organizations of the future will be run by “rational” Weberian decision-makers.

Causality

13Perhaps the most fundamental assumption of Western Philosophy and Science is the belief that there is a “causal chain” which underlies observed phenomenon and that the links in that chain can be discovered and understood, but more importantly, controlled, using the proper tools and methods. This belief is the basis of modern Empiricism, and is clearly a necessary condition for DS and SM, since without the ability to link specific decisions and actions to outcomes, it is fruitless - even impossible - to develop strategies to achieve a given objective. It is useful to remember, however, that this belief - as ubiquitous as it has become - is merely an artifact of a specific period in the development of Western Philosophy and Religion and is neither certain nor universally accepted (Burk, Litwin, 2009 ; Climo, Howells, 1976).

14Similarly, the modern Scientific Method which grew from these roots institutionalizes a belief in “causality” and produces results based on that assumption. Although this approach has undeniably produced countless important insights and innovations, it is not without drawbacks (Heineman, 1981). For example, given the complexity of most interesting phenomena and the need to “control” extraneous and exogenous factors, problems must be broken into extremely narrow pieces in order to use this method correctly. Consequently, experimental results apply to only a small part of a much larger question yet are routinely used to explain much broader phenomena. Further, by controlling various factors and examining phenomena out of context, results are often unrealistic - even within the narrow context they purport to explain. More importantly, however, by linking a series of isolated result into a complex theoretical chain, this approach reinforces the assumption of causality and creates the illusion that it is possible to understand and control basic phenomena (Dermer, Lucas, 1986; Thompson, Armstrong, Thomas, 1998). Unfortunately, as Schumpeter (1946) pointed out years ago, it may be (theoretically) possible to correctly identify the antecedents in each step of a “causal chain”, and yet still fail to understand the true links - if any - between an initial “cause” and an ultimate “effect”. We would argue that something much like this could explain why even the most carefully developed strategic plans and actions frequently encounter problems and unexpected consequences.

15Two final factors are worth noting. The first involves recent advances in Physics that have all but demolished the Cartesian view that “causes” and “effects” can be isolated and related in some meaningful way (Schwartz, Stapp, Beauregard, 2005; Stapp, 1999). The second comes from research in Biology, Chaos and Systems Theory and suggests that there may be an inherent “logic” in complex systems which is largely responsible for their changes over time (Auyang, 1999; Chen, Yu, 2003; Levy, 1994; Thietart, Forgues, 1995). This “force”, which may be seen as the opposite of Entropy (which works to destroy), is a basic property of all life and may account for such grand processes as Evolution and the unfolding of the Universe (Capra, 1996; Kauffman, 1995; Kirilyuk, 2002). In either case, these ideas brings into question the ability of individuals to predict and control outcomes, and pose of serious challenge to the basic theories and models of DS and SM.

Organizations

16It is perhaps unnecessary to comment on the challenges to classical OB since a great deal of research in SM over the past several decades has attempted to revise and extend these theories to better explain contemporary organizational phenomena (Barney, 1986; Burton, 2004; Hambrick, Cho, Chen, 1996; Kraatz, Zajac, 2001). Nevertheless, it remains true that continual advances in telecommunication and information and other technologies have fundamentally changed the nature, activities and processes of organizations (Brynjolfsson, Hitt, 2000; Clemons, Row, 1992). Going forward these factors - and the phenomena they inspire - are likely to pose an even greater challenge to classical theories of organization and control. For example, as individuals are able to instantaneously access and process unimaginable amounts of real-time data and communicate with anyone, anywhere, anytime, the traditional structures and processes used to control organizations become ineffective, and the traditional bases of power and authority are undermined (Middleton, 2008 ; Peters, Allouch, 2005). As the authors are wont to note, just a few decades ago the brainpower of the world’s most powerful corporations could be found in a few buildings on Park Avenue surrounded by secretaries and filing cabinets; now they are sitting in a Starbucks reading their e-mail. As a result, it becomes extremely difficult to create organizational structures and process that support - rather than inhibit - creativity and enable strategic decision-making and other critical activities to occur.

17A second interesting challenge to classic OB and SM comes from recent research in Biology and Life Science (Jawahar, McLaughlin, 2001; McKelvey, 1997). Particularly after Chernobyl, the role of mutation in evolutionary processes has been intensely studied with some unexpected results. This research confirms the emergence of new, stable forms in rapidly changing, hostile environments and has produced many other findings that were not readily predictable a priori (Aldrich, 2008; Slevin, Covin, 1997). This research has important implications for such basic theories as Contingency Theory (Thompson, 2011), Resource Dependency Theory (Bretherton, Chaston, 2005), and Organizational Ecology (Amburgey, Rao, 1996), and suggests that novel and unexpected organizational structures (and strategies) are likely to emerge and thrive in rapidly changing competitive landscape.

18Thus, much like Physics at the beginning of the 20th century, new ideas and phenomena pose a challenge to some of the most basic assumptions of theories of Economics and Organizational Behavior. But also like that field, the challenge of new ideas can lead to a fruitful period of innovation and discovery that spawns new, more powerful theories going forward.

Asygnosis: emergent unconscious knowledge

19If the most basic models and assumptions of Economic and Business theory are outdated, what should replace them? We propose a simple “model" that we believe provides a more accurate description and explanation of many new organizational and strategic phenomena. It is based on current research in Neuroscience which seeks to explain human behavior and decision-making based on the structure and function of the brain (Bechara et al., 1999; Hsu, et al., 2005; Koenigs, Tranel, 2007; Rilling, Sanfey, 2011; Sanfey, 2007). As noted above, these models have often provided the template for theories of decision-making and strategy formulation and implementation, and the organizational structures and processes that support these activities.

20Not so long ago, the brain was viewed as a “comparison engine” which broke complex problems into a series of hierarchical binary decisions (Kelso, 1997; MacKay, 1965). At each point in the decision chain, various options were analyzed and compared and the “best” option was selected. The ultimate decision was merely the final choice at the end of this process. Recent research, however, suggests that this is not the case. Decisions are not the end product of a structured, sequential process, but instead result from the (inter-) action of large numbers of neurons firing simultaneously across nodes. Consequently, there is no central locus of decision-making in the brain (or likely the firm), but rather a diffused panoply of activities that occurs in multiple locations at the same time. Further, these activities have no fixed structure, but result from the continuous formation and dissolution of complex neural networks (Duffau, 2006; Lewis, 2005; Pascual-Leone, et al., 2005; Theodoni, et al., 2011). These findings suggest the counterparts to our three core assumptions shown in Table 1.

Table 1: Basic Assumptions in OT and SM

Assumption

Rationality

Causality

Organization

Impact on OT/SM

Theory

Old Assumption

Maximize/optimize potential outcomes and “payoffs” given expected future states.

Links between “causes” and “effects” are knowable, and can be predicted and controlled.

Organizational structures, processes and rules as control mechanisms; Individual power based on position, knowledge and informal networks.

New Paradigm

“Optimal” outcome is expression of current knowledge within self-generating decision networks.

Outcomes based on summation of continuous “nano”-decisions and the creation and dissolution of emergent networks; Cannot accurately link “causes” with “effects” or predict outcomes a priori.

Structure and process as an inhibitor to creativity and action; “Illusion of control”; Power based on position in given asygnodic networks.

21Based on these assumptions we propose a simple framework which we call Asygnosis”. The word is constructed from Greek: a- (not) + syneiditi- (conscious) + gnosi- (knowledge, information); and describes the undirected emergence and demise of knowledge and other interconnected pathways that form around a specific idea or activity. We would argue that these Asygnodic” (+ diktyo - grid or network) networks describe many complex organizational activities, particularly decision-making and strategic planning. For example, decisions and strategies are not the outcome of a complex, structured set of discrete activities or processes, they are the product of the continuous (non-discrete) flow of information, ideas and impressions (memes”) along ever-changing communication pathways between individuals and/or machines. Much like the pathways between neurons in the brain, these networks are characterized by their constant formation, strengthening, weakening, and disappearance based on use and need. Note also that both the continuous flow of memes” and the constant reconfiguration of these networks takes place not only between individuals, but between individuals and machines, and the machines themselves without regard to firm or national boundaries. Thus, in its simplest form, Asygnosis combines four concepts: (1) A meme or memes - the ideas, impressions and images that are communicated; (2) A communication or other network through which the meme spreads; (3) The continuous, organic evolution of that meme(s) as it flows”; and (4) The plastic”, ad hoc nature of the network as it grows and dissolves.

22On the surface this may appear to be merely an extension of Network Theory; however, there are several key differences. First we argue that Asygnodic networks are self-generating and occur at all levels both within and outside a formal organization. Going forward the impact of these networks will only increase as barriers to communication and information flow decline, and individuals become more synchronized and enabled by new technologies. Second, and perhaps more importantly for SM, we argue that although (some of) the rules governing the nature and effects of Asygnosis may be discoverable post fact, the phenomenon itself is beyond a priori prediction or control because these rules are not fixed and new systems and networks are constantly being formed and dissolved based on their own inherent purpose, structure and logic. Third, we contend that much of what appears to be strategic planning and decision-making is actually the result of this process”. And finally, the external (competitive) environment at any given point in space and time – especially the models and constructs used to define it – is the manifestation of the combined impact of this activity.

Implications for SM and OB

23Clearly Asygnosis has important implications for SM theory and research, some of which are noted in Table 2. Since these are largely self-explanatory and relate to existing models and theories they are not discussed in detail. Instead we focus on three major implications of the framework. The first is obvious, but deserves restatement: Most organizational (and other formal) structures and processes work against the spontaneous action of Asygnodic Networks (ANs). In fact the very purpose of these mechanisms is to eliminate and control exactly the types of interactions these networks represent. For example, limiting access to information or people is a common means of controlling behavior in traditional organizations. Similarly, established procedures and rules are standard ways to achieve specific objectives, or so it would seem. But to the extent our framework is correct, these concepts now become a hinderance rather than a benefit, and must be removed, or at best applied to extremely specific activities that are routine and predictable. The same goes for barriers between individuals, firms and countries, since any impediment to the free flow of memes” or the creation and dissolution of asygnodic networks is problematic. The new mandate, therefore, is to facilitate – not limit – interaction. Besides, as history illustrates all too well, ideas and people are ultimately smarter than the means developed to control them.

24A second implication involves the “Illusion of Control” as we have called it. To some extent the appearance of control is an artifact of the empirical method; and although controlmay be possible for very short periods of time in regards to very narrow, structured activities, in the new paradigm, outcomes are the result of an enormous number of simultaneous events”, and as these nano-” decisions” and actions mass together over even short periods of time the ability to control or predict outcomes decreases accordingly. This explains why even the most carefully created strategic plans – even those with constant feedback and readjustment – typically encounter difficulties. Thus, strategic planning becomes like predicting the weather over a sustained period in all parts of the world. And even if one could gather and process the enormous amounts of real time data needed to accomplish this feat, controlling the weather everywhere over a sustained period is another matter. Thus, we must replace our faith in rationality, causality and control with a new faith in the underlying logic of complex self-organizing systems and cooperate with that logic by reducing barriers to expression, interaction and the flow of information.

25Third, it is worth noting that due to the “plasticity” and spontaneity of Asygnodic Networks, at any given moment they may move or change in an infinite number of directions. As a result, it is not possible to accurately predict their trajectories over longer time frames, nor may it be possible to effectively alter their course at any point along the way. While this would seem to suggest that any form of strategic planning is futile (and we are not arguing that it isn’t) it does not mean that the underlying nature of this phenomenon is immune to study. On the contrary, anything that can be learned about this “process” becomes incorporated into future ANs and the “memes” that flow through them. This in turn effects the evolution of these networks, and should have some unknown impact. What is not so useful is studying Asygnosis with the intent of controlling or predicting the process. Although this will also have some impact on its evolution, the end results are unlikely to be in the desired direction.

Table 2 : Potential Research Topics

Changes in Assumptions

Neurological Changes in the Individual caused by New Media and Technology.

Obsolescence of Weberian Form and Emergence of Non-Hierarchical Network Forms of Organization.

Dissolution of Firm Sovereignty and Globalization.

Neuroscience as disruptor.

IT as disruptor.

Global Homogeneity as a disruptor.

Focus of

Theory

The Individual

Model of decision making as continuous massively sourced process; Extension of behavioral research to account for strategic influence of “always connected; always at work” employee, with different value systems.

Human resources management without hierarchy; new forms of motivation; identification of new required skill sets. Provisioning and resourcing new generation of employees (infrastructure; work-life balance).

Study of elite networks spanning national and firm boundaries and their effect on formation of firm strategy; effects of memes.

Organization Theory

Behavior of individuals, including motivations, may be de-linked from hierarchical position in traditional models; re-thinking of employee empowerment and communication structure.

Change in ownership; responsibility ; and control of firm operations (“span of control”).

Examination of industrial networks and other arrangements implying firm as unit of analysis may be obsolete ; enrichment of contingency theory.

Decision Theory / Strategy Process

Decisions are not made the way they have been modeled. Development of new paradigms of decision making based on networks of decision “nodes” ; New paradigms of human behavior and value.

Study of decision making and solutions based on failure of Weberian assumptions and absence of centralized or hierarchical control ; re-thinking of causation.

Strategy as a continuous process ; decision making across complex industrial forms.

Asygnosidic networks

The “Lifecycle” of an Asygnodic Network

26Although Asygnosis was originally developed to explain Decision-making and Organizational processes, it is also useful for analyzing Social phenomena such as the formation, dissolution and effect of Social Networks that emerge around a specific topic or activity. Recent examples of these types of ANs include the so-called “Arab Spring”, Occupy Wall Street and even Wikipedia. Although these examples may appear to have little in common they share several important features associated with Asygnosis and ANs. First, they link a large number of people with diverse skills and knowledge from widely divergent locations and backgrounds into a fluid, interactive community interested in the same idea or activity. Second, they emerged and grew without traditional support and against conventional wisdom. Third, they were more useful in creating and sharing impressions and knowledge than developing strategies and coordinating action. And finally, even though they each had a profound impact, they evolved or dissolved over time to reflect contemporary realities and the needs of their members.

Figure 1: Asygnodic Networks can span multiple networks depending on the paths of memes

Figure 1: Asygnodic Networks can span multiple networks depending on the paths of memes

27Asygnodic Networks develop differently from traditional networks. Instead of being developed for a specific purpose, ANs are not structured or designed by any external entity. They do not have the same underlying logic. Whereas traditional networks are the result of planning and architecture, Asygnodic networks resemble living systems. They can grow in unexpected ways, and even move from place to place, causing shock waves as they spread (BAKIS, COMBES, 1991). The strength of traditional networks is that they are stable, efficient and generally reliable. ANs are more dynamic, and their scale and scope is driven by the flow of memes. They can appear and grow rapidly, and decline then dissolve just as rapidly, but also can remain stubbornly persistent. See Table 3.

28A key difference between ANs and traditional networks lies in how they are sustained by their own performance. Traditional networks are evaluated according to an external variable, usually efficiency. ANs, on the other hand, are driven by the flow of memes themselves. Traditional networks carry highly structured information, but ANs carry highly variable information. Each flow of a meme will influence the strength and persistence of the AN, whereas in a traditional network, there is no such effect. Since memes can jump from one domain to another, they can leave one networks onl to enter another. But when this happens, the AN is extended.

29Figure 1 shows three networks, l, n and m. Each of the three networks is composed of a number of nodes (1-5). Flowing from left to right, the network carries its traditional payload of information (a1-a5, a’1-a’5 and a’’1-a’’5). As pictured, a meme moving through network l reaches the third node then dives down to the n network arriving at n3, then proceeds to n4 and n5. A similar jump” is pictured for a meme leaving the m network at node m3, then jumping” to the n network and landing on n4 before proceeding. As these relationships strengthen with repetition, then the AN is formed of l1-l2-l3-n3-n4-n5. Network jumping” can be an essential element of inter-network bridges, such as found on the Internet. But with ANs, the networks can vary widely across media. For example, the l network might be television propaganda, the n network social media, and the m network word-of-mouth. This capability for network jumping”, then subsequent formation of a stronger network is one essential feature of asygnodic networks. Another example is found in an enterprise. Network n might be the formal chain of command in the organization. Network l might be the email system. Normally these two networks might work in harmony with one another. However, social media, network m, might start to be a media of communication between employees in the organization and customers, outside of regular channels. Or in another scenario, the reputation of a firm might be determined in networks not accessible to the enterprise. A third example of how asygnodic networks move would be seen if there were a sudden increase of social media chatter regarding product quality, then this network starts to intersect with the enterprise for a while, then it disconnects and moves on when the situation has changed or resolved itself.

30Typically, traditional networks are geographically bound because their purpose is to collapse specific time and space barriers between nodes. As such, they do not extend themselves, or change focus depending on the content they are carrying. Although they collapse geography, and allow people to communicate between distant locations, they remain stable because they are engineered around a single narrow purpose that may evolve, but only changes slowly, and only according to the external logic of efficiency. Ultimately, traditional networks have changed the balance of information on a global basis. See BAKIS (1987), « Géopolitique de l’information ». On the other hand, ANs are formed by riding on top of multiple traditional networks, so as to operate themselves as a network. So if geography is minimized with traditional networks, then ANs have the same effect, but only temporarily, and unpredictably. For example, massive social events that are enabled by pervasive social media in one region of the world can temporarily via ANs link together parties in another region and transfer memes, like a contagion, so that similar disturbances in a second region are magnified. But this effect can go away once the meme flow plays out its effect and subsides. The power relationship between nodes in a traditional network is more or less stable as designed. Locations have specific purposes, some are control points. This does not happen in ANs, which do not have a power structure tied to specific locations. There is no control point or hierarchy of information flow. The entire network simultaneously and not based on location can grow from any node at any time and these changes are driven by the internal logic of the content.

Asygnodic Social Networks

31The basic characteristics of Asygnodic Networks make them particularly well-suited for Social and Political purposes since these activities tend to be highly “charged” and therefore encourage individuals to express and share deeply personal and emotional reactions and impressions. The anonymous, real-time nature of ANs only enhances their appeal. But this also creates a number of potential problems. The first involves the nature of the “meme(s)” being shared and its tendency to “mutate” as it spreads through the network. Many Social and Political events, ideas and images are capable of producing extreme reactions in individuals and this often causes the network to spread and evolve beyond all reasonable expectations as individuals react and interact. The second problem involves the anonymous nature of ANs which in certain instances can encourage the worst type of group behavior, such as spreading rumors or innuendo, and even fear and panic through a (potentially global) community at hyperspeed. Finally, there is always the possibility that certain individuals will be incited to move beyond mere expression to action and violence. Once these types of effects emerge, there is little or no way to control them, and subsequent outcomes become highly fluid and unpredictable. A good example of this is a live clip of a Police Officer shooting a citizen without apparent cause (the “meme”). As these images spread through a community, they generate powerful reactions that only serve to attract more attention and reaction. As the network evolves and grows over time the original stimuli becomes less important, and members’ attitudes and responses become more entrenched and synchronized. This makes it very difficult to correct misinformation and change opinions over time, as the network begins reacting to reactions until it eventually burns itself out.

32Several factors may explain some of the effects of Asygnodic “Social” Networks. First, the constant exposure to media and other sources of “information” has made it difficult for most people to distinguish between true “information” and simple “noise”. As a result all things are perceived as being of equal value and importance. Second, the extensive use of new technologies - from cell phones and computers to video games and YouTube - has created a permanent state of sensory overload which demands constant stimulation. Various studies in Behavior and Neuroscience have found similar effects between this state and addiction to drugs such as crack and cocaine. Third, one of the impacts of constant exposure to new media is to dissociate the individual from both the image or “information” (i.e. “meme”) being viewed and from his or her own emotional response. As a result, over time individuals become increasingly disconnected from “reality” and see the world through the lenses of the networks they are part of. Further, emotional responses become repressed or misdirected and are not dealt with effectively as they arise. Instead, they build quietly inside the individual until something ultimately triggers them, causing an unpredictable, explosive reaction often to an unrelated and insignificant stimulus. And since the traditional linkages between specific memes (images, sounds, etc,) and specific emotional responses have been broken, it becomes increasingly difficult to predict the reaction a given “meme” will provoke. For example, a symbol of state power such as a national anthem or veteran is just as likely to incite hated and rage as patriotism, even in the patriotic. Thus, by disconnecting individuals from their emotions - or perhaps more accurately the understanding and control of their emotions, their reactions also become dissociated from the stimulus.

33But there is a more basic Social and Political problem with ANs that, and over time, may prove to be its greatest threat or hope. Because ANs are non-restrictive to both individuals and ideas, they provide a vehicle for the disenfranchised to express their thoughts, feelings, hopes and fears among themselves. When these groups become the vast majority of a specific community, nation or the world, ANs become an overwhelming force for change. When that “force” is pitted against an inflexible National or International Institutional Structure, the implications are both clear and frightening. Imagine the progress of the Revolutions in Eastern Europe during the 1980s if anything like Asygnodic Networks had been in widespread use when the Berlin Wall fell. And although Occupy Wall Street may have failed in its original objectives, nearly a decade after the fact it is hard to find anyone who openly supports Wall Street or the economic and political institutions which shelter it. Thus, ANs can provide a powerful counter-weight to an ossified Establishment that has become increasingly out of step with the hopes and needs of the majority of its citizens. Unfortunately, History suggests that the meeting of these two forces is rarely peaceful.

Table 3: Key Characteristics of Traditional and Asygnodic Networks

Type of Network

Asygnodic

Traditional

Characteristic

Purpose

Have no inherent logic understandable in advance; are not “structured” by anyone.

Structured for a specific reason.

Formation

Living systems; not directed or designed; expansion, shrinkage or dissolution takes place without direction and with no reference to external element.

Networks are generally a result of planning and architecture; expansion can be explained according to traditional measures like transaction costs; dissolution is a function of utility to external element such as cost/benefit.

Structure

Not designed; it follows its inherent logic; random; a priori not predictable.

It is designed and engineered to meet the demands of the purpose of the network; built around nodes.

Dynamism, Growth and Persistence

Dynamic and not necessarily persistent. The network can come and go, and even move around; can move through locations because they are formed by flows of memes.

Although they change, these networks are relatively stable, and their very utility rests in part on predictability and efficiency.

Relationship between content and network structure

Independent. Media-jumping memes move across channels without transforming themselves. The networks exist simultaneously across multiple media.

Dependent. There is a fixed relationship. Typically defined as being a single media e.g., fiber optic cable; organized structure; relationships; hierarchies of managers or traditional command and control systems. Hierarchy of nodes.

Membership

No control over membership or node identity.

Membership controlled; nodes carefully defined.

Content

Memes represent ideas which are configurations of concepts, not fixed packets of data or content; they move through networks and transit through inter-network barriers.

Usually designed to have uniform content and media, e.g., electronic signals, person-to-person communication (bi-directional), movement of goods and services (logistic networks).

Integrity

Not designed; content integrity not important since memes constantly evolve as they pass through network.

Designed for reliability and integrity. Built-in redundancy to ensure integrity of content.

Network Economics

Does not follow traditional economic theory because operation of networks not dependent upon utility or any other quantifiable metric.

Network utility is a function of quantifiable metrics; their sustenance is a function of perceived utility to external elements.

Asygnosis and Terrorist Networks

34For many of the reasons discussed above, Asygnodic Networks have much in common with emergent Terrorist Networks, and consequently, the Intelligence Community has taken an interest in the concept.

35Working with co-authors Blaine and McCreary, Roche (2015) applied Asygnodic principles to the growing threat of terrorism in the paper, The Cyber Intelligence Challenge of Asygnodic Networks”, which appeared in J. of Strategic Security. Several unique findings emerged. Perhaps the most important being that, although traditional methods of network analysis are useful ex post, it is extremely difficult to predict the evolution and impact of a terrorist network a priori. Once a terrorist attack occurs, it is often possible to use mathematic modeling, content analysis and similar techniques to identify both the information flow and membership of a particular network; but this is not necessarily helpful in predicting when a specific meme” will provoke a specific action - even in a network that is constantly being monitored. Thus, the authors stress the need to identify memes” that are highly correlated with terrorist actions, and study the factors which cause these stimuli to trigger” action in certain individuals. This is extremely challenging because the same meme” can produce widely divergent responses in different individuals. Nevertheless, since Asygnodic Networks are extremely hot”, the likelihood of emotional reaction is high, and over time membership in these networks may provoke corresponding (terrorist) actions in certain individuals. It is worth noting, however, that Asygnodic terrorist” networks have the same barriers to coordination and action of terrorist activities that they do for any other activity; and although they may provide a platform for recruiting and linking individuals around a specific idea or goal, translating that community into a coordinated strategy for action is another matter entirely.

36The evolutionary property of Asygnodic Networks is one reason they are so difficult to identify and track. Traditional techniques focus on the flow of information (or memes) within and between individuals and groups, which necessarily requires a more defined target (meme) and more structured relationships than are typically found in an evolving AN. This does not, however, mean that these techniques are without merit. Since ANs support the emergence of unconscious knowledge (i.e., latent feelings and attitudes toward a particular idea, image or subject), they are not in themselves vehicles for action. Action results from the secondary” effects a meme has on other, more established (sub-) networks, which may be susceptible to tracking. This occurs when one or more members of an existing (online or other) community are impacted by an emerging asygnodic meme and shares their impressions and reactions within other groups they participate in. Thus, in terms of the Neurological models discussed above, an AN may alter (i.e. lower or raise) the action potential within an established community by impacting the willingness of members to engage (or dis-engage) in action of some kind.

Key Differences with Traditional Network Theory

37On the surface “Asygnosis” and “Asygnodic Networks” may appear to be little more than Network Theory in a new guise; however, there are several important differences. The first involves the nature of the technologies that enable ANs to emerge and spread. By the first decade of the 21st century, computer-based Social Media and global telecommunication systems made it possible to link 10s and even 100s of millions of individuals in real-time to share ideas, reactions and images. The potential impact of such networks is profound and will only increase in the future as barriers to communication and information flow decline. Thus, given the potential scale and scope of Asygnodic Networks and their immediate, non-restrictive nature, they necessarily differ from their traditional counterparts.

38For example, traditional networks - even “informal” ones - are created for some basic purpose and their membership is often restricted based on an individual’s ability to contribute skills or knowledge to that objective. Asygnodic Networks, on the other hand, are self-generating and function more like living organisms as they use telecommunication (and other) systems to spread a meme(s) through the global population. As a result, ANs evolve in unpredictable ways since their purpose and membership are not fixed but continuously changing based on their own inherent purpose, structure and logic. This means that the evolution and impact of a specific AN cannot be “controlled” in the traditional sense or predicted a priori.

39Further, because the technologies that support Asygnodic Networks allow the real time transfer of sound and images as well as information, they reduce barriers to language and culture, and are extremely “hot” in the McLuhan sense. They also provide a strong sense of anonymity which encourages individuals to express deep-seated, emotionally charged reactions and impressions that would normally be filtered or self-censored in traditional social networks. As a result, ANs provide a vehicle for tapping what Jung and others have called the “Universal Unconscious”, enabling a global “collective consciousness” to emerge around a specific meme or activity.

40Finally, traditional networks - particularly those involving the transfer of knowledge and information - are highly concerned about maintaining the integrity of that information as it flows through the network, and employ sophisticated safeguards for this purpose. This is not the case with ANs ; on the contrary, as a meme(s) flows through an AN it tends to evolve or “mutate” much like a virus as individuals react and respond to this stimulus. For this reason, Asygnodic Networks are more useful to uncover and develop knowledge, than to reach conclusions, take decisions or create and execute strategies and actions.

Areas of future research and conclusions

Areas of Future Research

41The study of Asygnosis and Asygnodic Networks is a multi-disciplinary pursuit which crosses a number of academic boundaries. To some extent this explains why the concept has been greeted so sceptically, particularly in the Management and Strategy communities. As a result, it is perhaps useful to outline a few general areas of research that might be helpful to further define and develop these concepts. The first involves Neuroscience and the impact of new electronic communication and computer technologies on cognition, brain chemistry and behavior. It is already well established that various computer-based activities - from computer games to texting - can have a profound effect on the individual and his/her behavior; but more study is required to understand exactly why and how this occurs. Additionally, it would be useful to link specific types of “memes” with specific reactions, responses and behaviors, and to try to identify the various “triggers” that lead certain individuals to move from mere participation in an AN to actions outside these communities. The ultimate goal of this research is to discover to what extent the various technologies associated with ANs, versus the specific stimuli (“memes”) being viewed or communicated through those technologies, contribute to specific behaviors.

42The second area of research involves Psychology and related fields including Sociology. To some extent an AN may behave like a crowd or a mob, as individuals lose their inhibitions and begin to express their thoughts and emotions in an uncensored and unfiltered manner. This raises a number of interesting issues in both Individual and Group Psychology and Behavior that could help to explain why a specific AN may grow over time and how it is likely to evolve. These insights could be used to identify potentially problematic networks and those that might incite individual members to action or violence. Of course the Sociological implications and effects of ANs in general and specific types of ANs also provides a fruitful area of study.

43Finally, more research needs to be done on the causes and mechanism which enable ANs to grow and evolve. Although traditional Network Theory may be useful in this regard, the dynamic nature of ANs makes them more like living biological systems or organisms than traditional communication or knowledge networks. Thus, the application of various Biological and Evolutionary principles to the “life-cycle” of ANs could also be of great value.

Use of Geographical Tools to Study Asygnodic Networks

44The geography community has developed a number of tools and approaches to study networks. One of the first breakthroughs was the separation of traditional geographical measurements and the substitution of other metrics. Maps were re-drawn so that cities linked together by strong telecommunications linkages appeared closer than cities with weak telecommunications linkages. Instead of mapping telecommunications networks onto geographical space, geography was mapped on to telecommunications or information space. This was a breakthrough. Use of accepted geographic tools to study asygnodic networks presents several research challenges.

45First, the process of network identification is different. In the traditional model, a network is identified, and then much study is done to understand what content flows through the network and how it has an effect on society. This is because it is possible immediately to identify the underlying telecommunications infrastructure. But with asygnotic networks, the network is not visible easily and must be discovered. The only way to uncover the structure and extent of an AN is to follow the flow of memes. Meme mapping will lead to possibilities for derivation of the AN through which they flow. But the science of meme mapping to networks is in its infancy.

46Second, mapping the AN itself may not be as important as once thought. This is because of its dynamic nature. Its structure and operation one day may be changed the next. So it is more important to understand how ANs form, and the conditions that lead towards their creation. Getting a picture of the structure of a network is only a snapshot of a temporary configuration. It is more important to understand the dynamics of change, and learn the rules and patterns governing that change.

47Third, there is an important area of study involving the intersection of ANs and traditional networks. Above we gave the example of the effects of social media involving customers in an enterprise. But this is representative of a general class of problems that should be explored. Instead of simply mapping networks, we need to understand how to analyze the intersection of networks and the boundary effects as ANs move to link and de-link with traditional networks. What are the principles to be uncovered?

Some Final Thoughts About Asygnosis and Asygnodic Networks

48Over the past several decades, researchers from a number of fields have noted the emergence of a new type of “communication” network; one that enables the rapid transmission of ideas and images (memes) through a specific or general population. Of course, new technologies are the basis of these networks, but there is something else involved as well - something related to the way human beings now process and exchange information. Indeed, the very meaning of “information” has changed and individuals and groups have adapted to these changes in ways that are both unexpected and difficult to explain. Based on an extensive review of classical and contemporary research in a number of unrelated fields, this paper develops a framework to help explain the emergence and use of these “new” networks for various Organizational, Social and Political purposes. We call this phenomenon “Asygnosis”, and Asygnodic Networks are the new structures that use computer, telecom, Social Media and other social networks to propagate images, ideas and impressions (“memes”) through the global population.

49We have argued that new technologies have caused fundamental changes in the way individuals react to certain visual and ideological stimuli, and in the way they “communicate” those responses to others. As noted above, the excessive use of Social Media and other technologies has weakened the Individual’s awareness of and control over the linkages between a specific stimulus (meme) and the emotional (or “unconscious”) reaction it elicits. But even more importantly, new technologies have changed the ways individuals share their reactions with others by destroying traditional barriers to language, culture, and distance, and linking individuals in real-time through a complex web of interlocking “communities”. The end result is that “memes” now flow through the global population (or “consciousness”) much like a virus, changing (or “mutating”) as they spread from one community to another, and producing outcomes that are difficult to predict or control.

50Until recently ANs would have been seen as informal social networks for sharing ideas and news” within a closed community. As such, the relationships between individuals in the network would be fairly well defined and more or less permanent; and attitudes and reactions within the community would be similar and more or less stable. The emergence of Asygnodic Networks, however, allows members of any given community to be concurrent members of any number of unrelated groups. As a result, memes” now travel through the global population (consciousness) in a discontinuous, unpredictable way, leaving multifarious effects within the communities they touch. But more importantly, the meme” tends to distort or mutate” as it passes through the high frequency, electronic communication medium from one individual or community to another, leaving a vague, often unconscious emotional reaction in the individuals it touches. Thus, Asygnodic Networks are self-organizing knowledge networks that provide a vehicle for the expression of emerging - often unconscious - ideas, emotions and feelings within the evolving human consciousness. Since ANs are not permanent, they form and dissolve as the stimulus proceeds leaving few traces beyond an affective residue in those touched by the process.

51A basic concept in Psychology and Behavior is the Collective Unconscious which provides the Individual with a repository of basic human mental and emotional constructs and reactions, and contains knowledge about such universal concepts as God, the love of a Mother and Child, or Man and Woman, the fear of Nature and other forces, etc. Because it exists at a deep unconscious level, it is largely beyond language and cultural differences, and is capable of being influenced in ways that are not immediately apparent to the individual. For various reasons, ANs appear to operate at this level to some extent by enabling individuals to share their disparate emotional reactions to a stimulus (“meme”) in real-time in a more or less anonymous way. Until quite recently, the basis of this unconscious lexicon was fairly well defined and more or less stable; but there is good reason to believe that constant exposure to new technologies and key concepts and images have all but destroyed the traditional linkages between specific “memes” and basic emotional responses. Thus, as a “meme” spreads through a community or population it creates vastly different impacts in the individuals it touches which are hard to predict in advance, and may vary widely between communities, and even within the same individual. For example, an image of a mother and child is as likely to cause nausea as empathy or love. Similarly, an image of patriotism such as a flag or a wounded veteran may evoke animosity toward the government; or a clip of a pedestrian being hit by a motorist may elicit laughter rather than sympathy. Further, these reactions are likely to change as rapidly as the events and images that provoke them.

52The simple conclusion from all this is that Asygnosis and Asygnotic Networks provide a huge potential challenge to the Established Order and Global Institutional Structure. We have seen some early indications of this in Social and Political groups and movements around the World, but one would suspect that the true challenge of these networks will be the ultimate creation of a new Global Consciousness or Zeitgeist which will encompass everything from basic Economic and Political concepts to specific ideals of Beauty, Art and Music. While this process holds great promise for a better Future, because it will proceed from a deeply emotional level, it also brings with it the possibility of great conflict, particularly when pitted against inflexible Social and Political Institutions. Further, it will release a volatile, rapidly evolving, global stew of repressed emotions, which are largely beyond the knowledge and control of the individual and are perhaps best left undisturbed. But whatever the ultimate impact of Asygnosis and Asygnotic Networks may be, it’s too late to put the genie back in the bottle.

Haut de page

Bibliographie

ALAVI, M, Leidner, DE (2001), “Review : Knowledge management and knowledge management systems : Nonaka, I, 1994, A dynamic theory of organizational knowledge creation”, Org. Sci., 5(1), pp. 14–37.

ALBERT, DZ (1992), Quantum mechanics and experience (Harvard U. P., Cambridge, Mass.).

ALDRICH, H (2008), Organizations and environments (Stanford U. P.).

ALDRICH, H, PFEFFER, J, LEIDNER, DE (1976), “Environments of Organizations”, Ann. Rev. of Soc., 2, pp. 79–105.

ALEXANDER, LD (1985), “Successfully implementing strategic decisions” Long Range Plan., 18(3), pp. 91–97.

Amburgey, TL, Rao, H (1996), Organizational ecology: Past, present, and future directions”, Aca. of Man. J., 39(5), pp. 1265–1286.

Auyang, SY (1999), Foundations of complex-system theories: in economics, evolutionary biology, and statistical physics (Cambridge U. P.).

Back, KW (1971), Biological Models of Social Change”, Am. Soc. Rev., 36(4), pp. 660–667.

BAKIS H (1987), « Géopolitique de l’information », Que sais-je ?, Presses Universitaires de France, 128 p.

BAKIS H, COMBES Y (1991), « Vers l’entreprise-réseaux : chocs de cultures », in « Annales des Télécommunications », Tome 46, N° 11-12, pp. 620-631, Nov.-Déc. Issy-les-Moulineaux (3 fig., 47 ref.).

Barney, J (1991), Firm resources and sustained competitive advantage”, J. of Man., 17(1), pp. 99–120.

Barney, JB (1986), Organizational culture: can it be a source of sustained competitive advantage?”, Aca. of Man. Rev., 11(3), pp. 656–665.

Beach, BH (1975), Expert judgment about uncertainty: Bayesian decision making in realistic settings”, Org. behavior and human performance, 14(1), pp. 10–59.

Bechara, A, Damasio, H, Damasio, AR, Lee, GP (1999), Different contributions of the human amygdala and ventromedial prefrontal cortex to decision-making”, J. of Neuroscience, 19(13), pp. 5473–5481.

Bourgeois, LJ (1981), On the measurement of organizational slack”, Aca. of Man. Rev., 6(1), pp. 29–39.

Brehmer, B (1992), “Dynamic decision making: Human control of complex systems” Acta Psychologica, 81(3), pp. 211–241.

Brynjolfsson, E, Hitt, LM (2000), Beyond computation: Information technology, organizational transformation and business performance”, J. of Eco. Pers., pp. 23–48.

Burke, WW, Litwin, GH (2009), “A causal model of organizational performance and change”, Organizational change : A comprehensive reader, pp. 273–299.

Burt, RS (2000), The network structure of social capital”, Research in Org. Beh., 22, pp. 345–423.

Burton, RM (2004), Strategic organizational diagnosis and design: The dynamics of fit (Springer).

Capra, F (1996), The web of life: A new scientific understanding of living systems (Random House LLC).

Chandler, AD (1990), Strategy and structure: Chapters in the history of the industrial enterprise (MIT P.).

Chen, G, Yu, X (2003), Chaos control: theory and applications (Springer).

Clemons, EK, Row, MC (1992), Information technology and industrial cooperation: the changing economics of coordination and ownership”, J. of Man. Info. Sys., pp. 9–28.

Climo, TA, Howells, PGA (1976), Possible Worlds in Historical Explanation”, Hist. and Theory, 15(1), p. 1.

Cyert, RM, March, JG (2006), Behavioral Theory of the Firm”, in Organizational Behavior 2: Essential Theories of Process and Structure, Ed. JB Miner (M.E. Sharp, Inc., Armonk), pp 60-77.

Cyert, RM, Williams, JR (1993), “Organizations, Decision Making and Strategy: Overview and Comment”, Strat. Man. J., 14, pp. 5–10.

Daft, RL, Weick, KE (1984), “Toward a model of organizations as interpretation systems”, Aca. of Man. Rev., 9(2), pp. 284–295.

Dermer, JD, Lucas, RG (1986), The illusion of managerial control”, Accounting, Org. and Soc., 11(6), pp. 471–482.

DeSanctis, G, Gallupe, RB (1987), A Foundation for the Study of Group Decision Support Systems”, Man. Sci., 33(5), pp. 589–609.

Donaldson, T, Preston, LE (1995), The stakeholder theory of the corporation : Concepts, evidence, and implications”, Aca. of Man. Rev., 20(1), pp. 65–91.

Doz, YL, (1996), The evolution of cooperation in strategic alliances: initial conditions or learning processes?”, Strat. Man. J., 17(S1), pp. 55–83.

Drazin, R, Howard, P (1984), “Strategy Implementation: A Technique for Organizational Design”, Columbia J. of World Bus, 19(2), pp. 40–46.

Driscoll, JM (1964), Subjective Uncertainty in Decision-Making, University of Delaware.

Duffau, H (2006), Brain plasticity: from pathophysiological mechanisms to therapeutic applications”, J. of Clinical Neuroscience, 13(9), pp. 885–897.

Egelhoff, WG (1991), “Information-Processing Theory and the Multinational Enterprise”, J. of Int. Bus. Studies, 22(3), pp. 341–368.

Eisenhardt, KM (1989), “Making fast strategic decisions in high-velocity environments”, Aca. of Man. J., 32(3), pp. 543–576.

Eisenhardt, KM, Abaracki, MJ (1992), Strategic Decision Making”, Strat. Man. J., 13, pp. 17–37.

Fiol, CM, Lyles, MA (1985), Organizational Learning”, Aca. of Man. Rev., 10(4), pp. 803–813.

Garvin, DA (2012), The Processes of Organization and Management”, Sloan Man. Rev. 39.

Goswami, A, Reed, RE, Goswami, M (1993), The self-aware universe: how consciousness creates the material world 1st American ed. edition (Putnam, New York).

Grant, RM (1996), Toward a KnowledgeBased Theory of the firm”, Strat. Man. J., 17(S2), pp. 109–122.

Greenberg, J, Baron, RA (2008), Behavior in Organizations (Pearson/Prentice Hall, Upper Saddle River).

Greiner, A, Hanusch, H (1993), “A Dynamic Model of the Firm Including Keynesian and Schumpeterian Elements”, J. of Institutional and Theoretical Eco., 149(3), pp. 516–530.

Gurbaxani, V, Whang, SJ (1991), The impact of information systems on organizations and markets”, Com. of the ACM, 34(1), pp. 59–73.

Hall, R, TOLBERT, ST (2004), Organizations: Structure, Process and Outcomes, 9th edition (Prentice Hall, Englewood Cliffs).

Hambrick, DC, Cho, TS, Chen, M-J (1996), The influence of top management team heterogeneity on firms’ competitive moves”, Ad. Sci. Q., pp. 659–684.

Hambrick, DC, Mason, PA (1984), Upper Echelons: The Organization as a Reflection of Its Top Managers”, Aca. of Man. Rev., 9(2), pp. 193–206.

Heineman, MB (1981), “The obsolete scientific imperative in social work research”, Soc. Service Rev., pp. 371–397.

Hillman, AJ, Cannella, AA, Paetzold, RL (2000), The resource dependence role of corporate directors : Strategic adaptation of board composition in response to environmental change”, J. of Man. Studies, 37(2), pp. 235–256.

Hodgkinson, GP, Healey, MP (2011), Psychological foundations of dynamic capabilities: reflexion and reflection in strategic management”, Strat. Man. J., 32(13), pp. 1500–1516.

Hsu, M, Bhatt, M, Adolphs, R, Tranel, D, Camerer, CF (2005), Neural systems responding to degrees of uncertainty in human decision-making”, Science, 310(5754), pp. 1680–1683.

Huber, GP (1990), “A theory of the effects of advanced information technologies on organizational design, intelligence, and decision making”, Aca. of Man. Rev., 15(1), pp. 47–71.

Jarrell, TA, Wang, Y, Bloniarz, AE, Brittin, CA, Xu, M, Thomson, JN, Albertson, DG, Hall, DH, Emmons, SW (2012), “The Connectome of a Decision-Making Neural Network”, Science, 337(6093), pp. 437–444.

Jawahar, IM, McLaughlin, GL (2001), Toward a descriptive stakeholder theory: An organizational life cycle approach”, Aca. of Man. Rev., 26(3), pp. 397–414.

Jones, C, Hesterly, WS, Borgatti, SP (1997), A general theory of network governance : Exchange conditions and social mechanisms”, Aca. of Man. Rev., 22(4), pp. 911–945.

Johnson, JL, Wallace, DW (2009), Multichannels strategy implementation”, Dec. Sci., 40(4), p. 869.

Kaplan, S (2008), Framing contests: Strategy making under uncertainty”, Org. Sci., 19(5), pp. 729–752.

Kauffman, S (1995), At home in the universe: The search for the laws of self-organization and complexity (Oxford University P.).

Kelso, JAS (1997), Dynamic patterns: The self-organization of brain and behavior (MIT P.).

Kirilyuk, AP (2002), The universal dynamic complexity as extended dynamic fractality: causally complete understanding of living systems emergence and operation”, in Fractals in biology and medicine (Springer), pp. 271-284.

Koenigs, M, Tranel, D (2007), Irrational economic decision-making after ventromedial prefrontal damage: evidence from the Ultimatum Game”, J. of neuroscience, 27(4), pp. 951–956.

Kraatz, MS, Zajac, EJ (2001), How organizational resources affect strategic change and performance in turbulent environments: Theory and evidence”, Org. Sci., 12(5), pp. 632–657.

Kriger, MP, Solomon, EE (1992), Strategic Mindsets and Decision-Making Autonomy in U.S. and Japanese MNCs”, Man. Int. Rev., 32(4), pp. 327–343.

Kumar, R (2014), “Managing Ambiguity in Strategic Alliances”, California Man. Rev., 56(4), pp. 82–102.

Laibson, D, Zeckhauser, R (1998), “Amos Tversky and the Ascent of Behavioral Economics”, J. of Risk and Uncertainty, 16(1), pp. 7–47.

Lanzetta, JT, Driscoll, JM (1969), “”Effects of uncertainty and importance on information search in decision making” : Erratum”, J. of Personality and Soc. Psy., 11(2), pp. 186.

Levine, DS (2009), Brain pathways for cognitive-emotional decision making in the human animal”, Neural Networks, 22(3), pp. 286–293.

Levitt, B, March, JG (1988), Organizational learning”, An. Rev. of Soc., pp. 319–340.

Levy, D (1994), Chaos theory and strategy: theory, application, and managerial implications”, Strat. Man. J., 15(S2), pp. 167–178.

Lewis, MD (2005), “Self-organizing individual differences in brain development”, Developmental Rev., 25(3), pp. 252–277.

Livet, P (2010), Rational Choice, Neuroeconomy and Mixed Emotions”, Philosophical Transactions: Bio. Sci., 365(1538), pp. 259–269.

Lorange, P (1998), Strategy implementation : the new realities”, Long Range Plan., 31(1), p. 18.

MacKay, DM (1965), Cerebral organization and the conscious control of action”, in Brain and conscious experience (Springer), pp. 422-445.

Maital, S (2004), Daniel Kahneman: on redefining rationality”, J. of Socio-Eco., 33, pp. 1–14.

March, JG (1978), Bounded Rationality, Ambiguity, and the Engineering of Choice”, Bell J. of Eco., 9(2), pp. 587–608.

McKelvey, B (1997), Perspective-quasi-natural organization science”, Org. Sci., 8(4), pp. 351–380.

Middleton, CA (2007), Illusions of balance and control in an always-on environment: a case study of blackberry users”, Continuum: J. of Media & Cultural Studies, 21(2), pp. 165-178.

Mintzberg, H (1987), Crafting strategy (Harvard Bus. Sch. P.).

Mintzberg, H (1989), Mintzberg on management: Inside our strange world of organizations (Simon and Schuster).

Montague, PR (2007), “Neuroeconomics : a view from neuroscience”, Functional neurology, 22(4), pp. 219–234.

Nunamaker, JF, Applegate, LM, Konsynski, BR (1988), Computer-Aided Deliberation: Model Management and Group Decision Support: Special Focus Article”, Operations Res., 36(6), pp. 826–848.

Ouchi, W (1981), Theory Z : How American business can meet the Japanese challenge”, Bus. Horizons, 24(6), pp. 82–83.

Pascual-Leone, A, Amedi, A, Fregni, F, Merabet, LB (2005), The plastic human brain cortex”, Annu. Rev. Neurosci., 28, pp. 377–401.

Penrose, ET (1952), Biological Analogies in the Theory of the Firm”, American Eco. Rev., 42(5), pp. 804–819.

Peters, O, Allouch, SB (2005), Always connected: a longitudinal field study of mobile communication”, Telematics and Informatics, 22(3), pp. 239–256.

Pfeffer, J, Salancik, GA, Leblebici, H (1976), “The Effect of Uncertainty on the Use of Social Influence in Organizational Decision Making”, Ad. Sci. Q., 21(2), pp. 227–245.

Porter, ME (2011), Competitive advantage of nations: creating and sustaining superior performance (Simon and Schuster).

Powell, TC (2011), Neurostrategy”, Strat. Man. J., 32(13), pp. 1484–1499.

Priem, RL, Butler, JE (2001), Is the resource-based view” a useful perspective for strategic management research?”, Aca. of Man. Rev., 26(1), pp. 22–40.

Reagans, R, McEvily, B (2003), Network structure and knowledge transfer: The effects of cohesion and range”, Ad. Sci. Q., 48(2), pp. 240–267.

Rilling, JK, Sanfey, AG (2011), The Neuroscience of Social Decision-Making”, Ann. Rev. Psychol., 62(1), pp. 23–48.

ROCHE, EM, BLAINE, MJ, MCCREARY, J (2015), “The Cyber Intelligence Challenge of Asygnodic Networks”, J. of Strategic Security, 8(3), pp. 107-136.

ROSENZWEIG, PM, Singh, JV (1991), “Organizational Environments and the Multinational Enterprise”, Aca. of Man. Rev., 16(2), pp. 340–361.

ROTH, K, SCHWEIGER, DM, MORRISON, AJ (1991), “Global strategy implementation at the business unit level: Operational capabilities and administrative mechanisms”, J. of Int. Bus. St., pp. 369–402.

SANFEY, AG (2007), Social decision-making: insights from game theory and neuroscience”, Science, 318(5850), pp. 598–602.

SCHUMPETER, JA (1946), “The Decade of the Twenties”, American Eco. Rev., 36(2), pp. 1–10.

SCHWARTZ, JM, STAPP, HP, BEAUREGARD, M (2005), “Quantum physics in neuroscience and psychology: a neurophysical model of mind‚Äìbrain interaction” Philosophical Trans. of the Royal Soc. B : Biological Sciences, 360(1458), pp. 1309–1327.

SCOTT, JE, VESSEY, I (2002), Managing risks in enterprise systems implementations”, Comm. of the ACM, 45(4), pp. 74–81.

SENIOR, C, LEE, N, BUTLER, M (2011), Organizational Cognitive Neuroscience”, Org. Sci., 22(3), pp. 708–711.

SIAKANTARIS, N (2000), Experimental economics under the microscope”, Cambridge J. of Eco., 24(3), pp. 267–281.

SIEGEL, S (1957), “Level of aspiration and decision making”, Psychological Rev., 64(4), pp. 253.

SIMON, HA (1964), On the concept of organizational goal”, Ad. Sci. Q., 9(1), pp. 1–22.

SLEVIN, DP, COVIN, JG (1997), Strategy formation patterns, performance, and the significance of context”, J. of Man., 23(2), pp. 189–209.

STANLEY, M (2014), From Ought to Is: Physics and the Naturalistic Fallacy”, Isis, 105, pp. 588–595.

STANTON, AA, DAY, M, Welpe, IM (2010), Neuroeconomics and the Firm (Edward Elgar Publishing).

STAPP, HP (1999), Attention, intention, and will in quantum physics”, J. of Consciousness St., 6(8-9), pp. 8–9.

SYMONDS, PM (1936), Making a decision”, in Education and the psychology of thinking, Ed. PV SYMONDS (McGraww-Hill Book Company, New York), pp. 157-163.

TAYLOR, FW (1914), The Principles of Scientific Management (Harper).

THEODONI, P, KOVACS, G, GREENLEE, MW, DECO, G (2011), Neuronal adaptation effects in decision making”, J. of Neuroscience, 31(1), pp. 234–246.

THIETART, R-A, FORGUES, B (1995), Chaos theory and organization”, Org. Sci., 6(1), pp. 19–31.

THOMPSON, JD (2011), Organizations in action: Social science bases of administrative theory (Transaction Publishers).

THOMPSON, SC, ARMSTRONG, W, THOMAS, C (1998), Illusions of control, underestimations, and accuracy: A control heuristic explanation”, Psy. Bulletin, 123(2), pp. 143–161.

TSAI, W, GHOSHAL, S (1998), Social capital and value creation: The role of intrafirm networks”, Aca. of Man. J., 41(4), pp. 464–476.

UMBLE, EJ, HAFT, RR, UMBLE, MM (2003), “Enterprise resource planning: Implementation procedures and critical success factors”, Eur. J. of operational res., 146(2), pp. 241–257.

VOLK, S, KÖHLER, T (2012), “Brains and games applying neuroeconomics to organizational research”, Org. Res. Methods, 15(4), pp. 522–552.

WEBER, M (1922), Wirtschaft und Gesellschaft.

WEISBERG, DS, KEIL, FC, GOODSTEIN, J, RAWSON, E, GRAY, JR (2008), The seductive allure of neuroscience explanations”, J. of cognitive neuroscience, 20(3), pp. 470–477.

Wrona, T, Ladwig, T, Gunnesch, M (2013), Socio-cognitive processes in strategy formation - a conceptual framework”, Eur. Man. J., 31(6), pp. 697–705.

Haut de page

Annexe

ABBREVIATIONS

ANs : Asygnotic Networks

DS : Decision Science

DSS : Decision Support Systems

IT : Information Technology

OB :Organizational Behavior

SM : Strategic Management

Haut de page

Table des illustrations

Titre Figure 1: Asygnodic Networks can span multiple networks depending on the paths of memes
URL http://netcom.revues.org/docannexe/image/2186/img-1.jpg
Fichier image/jpeg, 48k
Haut de page

Pour citer cet article

Référence papier

Edward M. Roche et Michael J. Blaine, « Asygnosis and asygnotic networks », Netcom, 29-3/4 | 2015, 419-450.

Référence électronique

Edward M. Roche et Michael J. Blaine, « Asygnosis and asygnotic networks », Netcom [En ligne], 29-3/4 | 2015, mis en ligne le 23 mai 2016, consulté le 24 avril 2017. URL : http://netcom.revues.org/2186 ; DOI : 10.4000/netcom.2186

Haut de page

Auteurs

Edward M. Roche

Columbia Institute for Tele-Information, Columbia Business School, New York, USA; Email: emr96@columbia.edu

Articles du même auteur

Michael J. Blaine

Email: HerbertOYardley@gmail.com

Articles du même auteur

Haut de page

Droits d’auteur

Licence Creative Commons
Netcom – Réseaux, communication et territoires est mis à disposition selon les termes de la licence Creative Commons Attribution - Pas d'Utilisation Commerciale - Pas de Modification 4.0 International.

Haut de page
  • Logo NETCOM Association
  • Logo IGU / UGI
  • Logo Comité national français de géographie (CNFG)
  • Logo UMR 6266 - IDEES Le Havre
  • Logo ARTDev (UMR 5281)
  • Logo DOAJ
  • Logo Heloise
  • Revues.org