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The impact of cognitive machines on complex decisions and organizational change

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Abstract

Humans and organizations have limitations of computational capacity and information management. Such constraints are synonymous with bounded rationality. Therefore, in order to extend the human and organizational boundaries to more advanced models of cognition, this research proposes concepts of cognitive machines in organizations. From a micro point of view, what makes this research distinct is that, beyond people, it includes in the list of participants of the organization the cognitive machines. From a macro point of view, this paper relies on the premise that cognitive machines can improve the cognitive abilities of the organization. From such perspectives, it presents rationale and principles of a class of cognitive machines with capabilities to carry out complex cognitive tasks in organizations. It also introduces analyses of the cognitive machines in organizations through theories of bounded rationality, economic decision-making, and conflict resolution. The analyses indicate that these machines can solve or reduce intra-individual and group dysfunctional conflicts which arise from decision-making processes in the organization, and thus they can improve the degree of organizational cognition. From all these backgrounds, this research outlines implications of cognitive machines for organizations.

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Notes

  1. The organizations in this paper satisfy the concept of open-rational systems (Nobre 2005; Scott 1998) and also the perspective of economic organizations (Milgrom and Roberts 1992). This type of organization integrates participants, technology and goals into a coordinative social structure in order to cope with the environment. They are highly formalized organizations that pursue specific goals and that produce goods and services.

  2. The environment includes information, consumers and stakeholders, other organizations like buyers and suppliers, networks of organizations, institutions, market regulators, the whole economy, cultural values and natural resources (Milgrom and Roberts 1992).

  3. In resume, knowledge management (KM) subsumes a range of practices used by organizations to identify, create, represent, and distribute knowledge, either embodied in individuals or embedded in organizational processes or practice, for reuse, awareness and learning (Ichijo and Nonaka 2006). KM efforts overlap with organizational cognition, and may be distinguished from by a greater focus on the management of knowledge as a strategic asset and a focus on encouraging the exchange of knowledge. In such a view, organizational cognition supports KM with cognitive processes that involve creation, organization and storage of knowledge.

  4. Organizational cognition, intelligence, autonomy and complexity, along with organizational learning, are organizational abilities. Their concepts are presented in (Nobre et al. 2009).

  5. This paper regards autonomy as the ability of individuals, collectives, systems, and organizations to act through the use of cognition.

  6. Simon and Zadeh played a counterpart task in the literature by proclaiming the lack of qualitative and quantitative approaches to cope with complex problems (where human behavior, emotions and cognition are key factors). In his theory of bounded rationality, Simon called for new approaches which could extend the methods of decision analysis used by economists to a more realistic scenario on human decision-making (Simon 1982a, b, 1997a). In his work about systems theory, Zadeh pointed out the need for new mathematics in order to narrow the gap of understanding between the analysis of non-living and living systems (Zadeh 1962). This paper advocates that such a new approach (as proclaimed by bounded rationality and general systems theorists) emerged with the advent of fuzzy systems theory (Zadeh 1965, 1973) and its derivatives on computing with words and perceptions (Zadeh 1999, 2001) along with soft computing (Zadeh 1994, 1997).

  7. This analysis is comparatively similar to the classical imitation game of Alan Turing (Turing 1950).

  8. The subject of emotions in organizations is left for further research. Perspectives about this topic can be found in (Bagozzi et al. 1998; Fineman 1993; Goleman 1994; Keltner and Gross 1999; Keltner and Haidt 1999; Plutchik 1982; Scherer 1982).

  9. Perception is the process through which sensations are interpreted; using knowledge and understanding of the environment, so that they become meaningful experiences (Bernstein et al. 1997). In the literature, perceptions can also be found as synonymous with a percept or a set of percepts, where a percept represents a class of objects with fuzzy boundaries (Zadeh 2001).

  10. Concepts are categories of physical and abstract objects with common properties like the attributes of color (red, yellow, green, etc), size (small, medium, large, etc), etc. A concept may be regarded as a percept recognized and classified into a category.

  11. Cognitive machines manipulate complex symbols in the form of words, propositions and sentences of natural language which are descriptions of percepts and concepts. Such complex symbols are codified through the principles of linguistic variables and fuzzy granulation, fuzzy sets and membership functions, and fuzzy generalized constraints; and they are manipulated through the mechanisms of fuzzy logic and fuzzy constraint propagation (Zadeh 1973, 1975, 1976, 1999). Therefore, such machines can manipulate more complex symbols than other machines whose base of computation is the classical set theory (Halmos 1960) and crisp granulation.

  12. Briefly, percept and concepts are alike when a percept is recognized and classified into a category. For example, by saying that Brazil is large, one is assuming that the size of the Brazilian land is classified as large, and in fact, large is a concept. For simplicity, percepts and concepts are treated as synonymous throughout this paper.

  13. Theories of modal and amodal-symbol systems in cognition and perception are presented in (Barsalou 1999).

  14. In the logic of consequences, actions are selected by evaluating their expected consequences for the preferences of the actor. It is related to the conception of calculation and analysis. In the logic of appropriateness, actions are matched to situations by means of rules. It involves conceptions of experience, roles, intuition and expertise (March 1994; March and Simon 1993).

  15. The complexity of the environment is contingent upon the level of uncertainty that it represents to the organization. Similarly, the complexity of a task environment is contingent upon the level of uncertainty that it represents to the organization during task execution and completion. Therefore it can be asserted that the greater the level of environmental complexity, the greater is the level of environmental uncertainty that the organization confronts and needs to manage (Nobre et al. 2009).

  16. Genetic programming for instance is a paradigm which has provided a profound impact on the design of software programs capable of generating tangible replicas and with enough ability to perform at least similar functions (Koza 1992).

  17. Consciousness concerns mental states of being aware of ourselves and our environment. It assumes the awareness of our own mental processes, thoughts, feelings and perceptions. Consciousness states can vary from deep sleep to alert wakefulness (Bernstein et al. 1997).

  18. People’s motives in organizations involve consciousness. “To be conscious that we are perceiving or thinking is to be conscious of our own existence”: Aristotle (384BC–322BC).

  19. Concepts of technical, managerial, institutional and worldwide levels of the organization and their context into Hierarchic Cognitive Systems are proposed in (Nobre et al. 2009). In such a case, levels are stages or positions in the organization hierarchy, and levels of analysis are perspectives about the organization and its roles taken by someone from different positions in the organization hierarchy. Moreover, in the context of information systems, levels can also be synonymous with hierarchy of information that comprises operational, managerial and strategic information systems.

  20. Cognitive Information Systems (CIS) are Information Management Systems (IMS) that pursue high degrees of cognition, intelligence and autonomy. They are particular classes of cognitive machines, and they are designed to participate in the organization by performing cognitive tasks of all levels and by fulfilling managerial roles in all the layers of the whole enterprise (Nobre et al. 2009).

  21. Briefly speaking, immersiveness represents the ability of an organizational system to interact with customers (either people or machines) in a friendly way, by immersing them into the organization through approaches such as virtual reality, simulation or real world operations (Nobre et al. 2009).

  22. A commonsensical knowledge base consists of a set of rules (conditional statements), and its design involves the integration and storage of propositions and mental models of the participants in the group into a common memory (storage device). It represents an attempt to satisfy (satisfice) the perspectives of a group and to attend some criteria of design. A commonsensical decision involves a process with access to a commonsensical knowledge base. It attempts to make choices and to provide outcomes which satisfy (satisfice) the opinions of the group and the criteria of design.

  23. The field of machine learning is concerned with the engineering of computational systems that automatically change and improve through experience (Mitchell 1997). Neural computation (Hertz et al. 1991), soft computing (Zadeh 1994), adaptive fuzzy systems (Wang 1994), evolutionary computation and genetic algorithms (Bäck et al. 2000; Fogel 2000), along with genetic programming (Koza 1992) are among the main disciplines which can be used to design and to aggregate learning processes to machines.

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Authors and Affiliations

  1. Innovation Technology Enterprise, Rua Irenio Pereira de Andrade 458, Montes Claros, MG, 39401-353, Brazil

    Farley S. Nobre

  2. The School of Mechanical Engineering, The University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    Andrew M. Tobias

  3. Birmingham Business School, The University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    David S. Walker

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Correspondence to Farley S. Nobre.

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Nobre, F.S., Tobias, A.M. & Walker, D.S. The impact of cognitive machines on complex decisions and organizational change. AI & Soc 24, 365–381 (2009). https://doi.org/10.1007/s00146-009-0207-4

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