Journal of Evaluation in Clinical Practice ISSN 1356-1294
Complexity and health professions education: a basic glossary jep_1503
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Stewart Mennin PhD Professor Emeritus, Department of Cell Biology and Physiology, Assistant Dean Emeritus, Educational Development and Research, University of New Mexico School of Medicine, Albuquerque, New Mexico, USA
Keywords complexity, definitions, terminology Correspondence Dr Stewart Mennin Department of Cell Biology and Physiology Educational Development and Research University of New Mexico School of Medicine Albuquerque, NM 87131 USA E-mail:
[email protected]
Abstract Rationale and aims The study of health professions education in the context of complexity science and complex adaptive systems involves different concepts and terminology that are likely to be unfamiliar to many health professions educators. A list of selected key terms and definitions from the literature of complexity science is provided to assist readers to navigate familiar territory from a different perspective. Terms and concepts include agent, attractor, bifurcation, chaos, co-evolution, col-
lective variable, complex adaptive systems, complexity science, deterministic systems, dynamical system, edge of chaos, emergence, equilibrium, far from equilibrium, fuzzy boundaries, linear system, non-linear system, random, selforganization and self-similarity.
Mennin Consulting & Associates Inc. htttp://www.menninconsulting.com Accepted for publication: 8 June 2010 doi:10.1111/j.1365-2753.2010.01503.x
Complexity is a different paradigm with unfamiliar terminology and concepts not easily or intuitively grasped. A glossary of common terms frequently encountered is offered to assist readers navigating an unfamiliar literature. Common usage of the word complexity denotes something that is difficult to understand or explain. Webster’s New Collegiate Dictionary [1] defines complex as ‘a group of obviously related units of which the degree and nature of the relationship is imperfectly known.’ It is important to distinguish between complicated and complex [2]. A system composed of a large number of components that can be described completely based on its individual constituents is complicated. Computers, jet planes and rocket ships are complicated. A system composed of many diverse components is complex if the interactions among the components are such that the system as a whole cannot be understood by analysing its components [3]. The definitions given below are a composite derived from the literature [2–19]
cell, a medical student, a teacher, etc. Individual agents interact at the local level and cannot know the system as a whole nor does a central agent have responsibility for overall control of the system.
Attractor An attractor is a trajectory of a pattern or activity in time in a region of space that ‘appears’ to draw the energy of a system to it. A helpful metaphor is a basin or a lake in a valley into which rain water flows after making its way down the various possible pathways of a surrounding mountain range. The water is said to be attracted to the basin or lake. Similarly, blood glucose levels fluctuate based on many different variables (carbohydrate intake, insulin levels, exercise, sensitivity to insulin, etc.). The pattern of the trajectory of blood glucose values over time has a particular range determined by the interactions among the multiple variables in the environment.
Bifurcation Agent Something that takes part in an interaction and is itself subsequently changed: a person, a society, a molecule, a plant, a nerve 838
A relatively abrupt change that occurs when some parameter(s) reaches a critical level (far from equilibrium at the edge of chaos) resulting in the emergence of a new state and pattern (a new
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attractor). The action potential of a neuron, the onset of puberty, the spike in gonadotropin releasing hormone leading to ovulation, and earthquakes are examples.
Chaos The apparent absence of order in a system that is highly sensitive to initial conditions (small fluctuations that disturbed the system lead to big changes). The weather is a chaotic system in which particular specific states are unpredictable; yet, the broad range of those states is predictable.
Co-evolution Co-evolution refers to the coordinated and independent evolution of two or more systems. Participating agents change, evolve, as a result of their interaction. Curriculum and assessment co-evolve, one influencing the other. The doctor–patient relationship co-evolves as one interacts with the other. Learners co-evolve through the exchange of differences.
Glossary of complexity terms
Deterministic systems A system in which particular states follow from, or are determined by, previous ones. Deterministic systems are in contrast to stochastic systems where future behaviour is independent of previous behaviour.
Dynamical system A complex interactive system evolving over time through multiple modes of behaviour and following certain rules. Physiological systems such as the heart and the brain are dynamical.
Edge of chaos A critical phase that occurs where it is not possible to predict outcomes with certainty. The possibility for the emergence of new, adaptive patterns is at a maximum at the edge of chaos. A critical threshold where self-organization and emergence are heightened: the adjacent possible.
Emergence Collective variable (order parameter) A condition or state in which many agents interacting in an uncoordinated way coalesce into an ordered or coordinated pattern. People at a dance party are milling around. When the music begins, they pair up or dance in groups forming coordinated patterns. A collective variable represents a decrease in the degrees of freedom among numerous agents. The spin of hydrogen atoms becomes coordinated under the influence of a magnetic field in magnetic resonance imaging. A wave of fans in a football stadium is a collective variable.
Complex adaptive systems ‘. . . a collection of individual agents who have the freedom to act in ways that are not always totally predictable, and whose actions are interconnected such that one agent’s actions change the context for other agents’ [20] Nerve cells, the immune system, the stock market, clinicians, patients, communities and health care systems are complex adaptive systems. A complex adaptive system adapts (learns) in response to changing conditions and thus can be said to have a history.
Complexity science A collection concepts and principles for the study of open systems that have nonlinear dynamical, self-organizing and emergent properties. Complexity science is the study of the dynamics of patterns and relationships rather than objects and substance in systems that are open and far from equilibrium. Complexity science focuses on processes and interactions of local agents that result in the emergence of new patterns as a whole.
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The arising of new, unexpected structures, patterns, properties or processes in a self-organizing system. Emergent phenomena exist on a higher level than the lower-level components that give rise to the emergents. For example, fertilization gives rise to a new emergent structure. The music that emerges from the coordinated interaction and exchanges among musicians in a jazz group and the learning that occurs as a result of the re-organization of structure leading to new patterns and possibilities.
Equilibrium A system that tends to remain at the status quo, for example, some traditional medical schools.
Far from equilibrium A system in which energy is exchanged across open (indeterminate, fuzzy) boundaries. Far from equilibrium states are an essential prerequisite condition for self-organization. Living systems exist at the edge of chaos. Examples include situated cognition, metabolism, a new set of organizational rules.
Fuzzy boundaries A demarcation, barrier or separation that is open and permeable allowing the exchange of energy between systems and between a system and its environment. A cell membrane, ground rules for group interaction, culture, ethics and a conceptual framework are some examples.
Linear system A system in which the relationship between variables can be plotted as a straight line. Small changes result in small effects and large changes result in large effects. Linear systems are those in 839
Glossary of complexity terms
which the results of a change are usually predictable, for example, a thermostat resetting a heater in response to a change in temperature.
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Non-linear system A system in which small changes can result in large effects and large changes may result in no or in small effects. In non-linear systems, the results of changing one factor are unpredictable yet may still be replicable. For example, heart dynamics plotted along a Starling Curve, the weather, the stock market, the release of neurotransmitters, the election of a new president.
Random Systems in which the results of any action are unpredictable are said to be random. If the exact starting circumstances were recreated in a random system, the result at any given subsequent time would be different. Rolling a pair of dice.
Self-organization A process in a complex system whereby new structures, patterns and properties arise (emerge) without being externally imposed on the system. There is no ‘self’ in self-organization and there is not a central hierarchical command and control centre. Examples include a flock of birds flying in formation, the a pattern of the daily arrival of food throughout a major city, learning, the daily stock market values.
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Self-similarity
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A pattern that exhibits identical or similar characteristics at different scales or orders of magnitude. Examples include the branching structure of trees and lungs, and the geographic patterns of a coastline. Fractals are an example of self-similarity.
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References 1. Webster (1979) Webster’s New Collegiate Dictionary. Springfield: G. & C. Merriam Company. 2. Glouberman, S. & Zimmerman, B. (2002) Complicated and complex systems: what would successful reform of Medicare look like?
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