Manuscript 2007-4-17 Perspectives on Team Dynamics Meta Learning and Systems Intelligence C

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http://www.systemsintelligence.hut.fi [accessed 2007-04-17]
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consequences of different interventions. Models of social interaction allow us to analyze how micro-level phenomena can influence macro-level outcomes.
Manuscript 2007-4-17
Perspectives on Team Dynamics: Meta Learning and Systems Intelligence
Jukka Luoma, Raimo P. Hämäläinen and Esa Saarinen jukka.luoma@hut.fi, raimo@hut.fi, esa@hut.fi Systems Analysis Laboratory Helsinki University of Technology
We see the concept of a system as a useful one in understanding human action in dynamic settings. Today, many organizational studies describe organizational phenomena by drawing analogies to concepts which originate in the field of mathematical modeling of dynamical systems. The use of systems studies ranges from quantitative to qualitative modeling; from numerical modeling (e.g. Sterman, 2000, 2002) to making simulation experiments which are considered analogous to organizational phenomena (e.g. Morel and Ramanujam, 1999; Stacey, 1995, 2001, 2003) and identifying organizations as systems (e.g. Senge, 1990; Senge et al., 1994). Complexity studies have also been suggested as tools to make sense of organizational phenomena (see e.g. Andersson, 1999). Yet opinions about the real usefulness of complex systems studies in organizational life vary (see e.g. Stacey et al., 2000). We believe that mathematical modeling of human systems does, indeed, provide a convenient way of analyzing the systems’ behavior and testing the possible
Corresponding author:
Jukka Luoma Helsinki tems Analysis Laboratory, P.O. Box 1100, 02150 HUT, Finland
Email: jukka.luoma@tkk.fi Tel.: Fax: +358 9 451 3053 +358 9 451 3096
Key words: team interaction, meta learning, systems intelligence, chaotic dynamics, Lorenz attractor
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Introduction
The perspective taken in Systems Intelligence1 (SI) (Saarinen and Hämäläinen, 2004; Hämäläinen and Saarinen, 2006, 2007) is the acknowledgement that we are always embedded in and a part of systems involving interaction and feedback, which we cannot escape but in which we can take intelligent actions. The study of systems intelligence is concerned with the behavioral intelligence of human agents in systemic environments. Systems intelligence looks for efficient ways for an individual to change her own behavior in order to influence the behavior of a system in different environments.
The literature on the modeling of social interaction is extensive. It dates back to the early work of Herbert A. Simon (1952) who illustrated how mathematical modeling could be used in “clarifying of concepts” of a theory, and in “derivation of new propositions”. Here, we only list some recent studies, which in our view have connections to the topic of this paper. Robert Axelrod (1984) used game theoretical experiments to show that under suitable conditions cooperation can emerge without a central authority. Young (2001) built a dynamical model of conformity based on Blume’s (1993) work on strategic interaction. Gintis et al. (2005) used game theoretical models and experimental game theory to study decision making in social interaction and the evolution of cooperation. Collins and Hanneman (1998) suggested a common framework for modeling the theory of interaction rituals (Collins, 1981, 2004). Gottman et al. (2002a, 2002b) developed nonlinear difference equations to model marital interaction as well as to design of counseling interventions for unhappy couples. Losada (1999; Losada and Heaphy, 2004) and his associates used nonlinear differential equations to describe team behavior. They paid particular attention to the potentially chaotic behavior of the model, and to the interpretation of that behavior. They discussed the model behavior in connection with the concept of meta learning which, as introduced by Losada (1990), refers to conversational, or micro-behavioral competence of teams. What is common to all of these studies is that they all use modeling as a tool to understand phenomena related to the dynamics of human interaction. This paper focuses on Losada’s research on team interaction and in particular, on the characteristics of the dynamical model proposed by him.
Manuscript 2007-4-17
Abstract
Losada (1999) observed management teams develop their annual strategic plans in a lab designed for studying team behavior. Based on these findings he developed a dynamical model of team interaction and introduced the concept of Meta Learning (ML) which represents the ability of a team to avoid undesirable attractors. This paper analyzes the dynamic model in more detail and discusses the relationship between meta learning and the new concept of Systems Intelligence (SI) introduced by Saarinen and Hämäläinen (2004). In our view, the meta learning ability of a team clearly represents a systems intelligent competence. Losada’s mathematical model predicts interesting dynamic phenomena in team interaction. However, our analysis shows how the model also produces strange and previously unreported behavior under certain conditions. Thus, the predictive validity of the model also becomes problematic. It remains unclear whether the model behavior can be said to be in satisfactory accordance with the observations of team interaction.