Failed Feedback: Barriers to Behaviour Change

Why does feedback fail? As I’ve discussed previously, learning (or behaviour change) is a feedback process. According to Sterman (1), there are two important requirements that must be met in order for learning (behaviour change) to be successful. First, the overall cycle of the feedback loops must occur within an appropriate time frame in order for the information to still be relevant. Additionally, each link in the feedback loop must be successful of itself; although the feedbacks exist, they often do not function very well. Today’s post will focus on the potential barriers that can affect each component of the feedback loop as discussed by in his article “Learning in and about complex systems”. (Delays have been discussed briefly in a previous post and will explored again in future ones).

Potential problems can arise at each of the links in the feedback loop (Figure 1). The information we receive may be inaccurate or even missing. Inaccuracies can occur when we try to measure or record the information. We, or others, may introduce bias, distortion, or selective perception. The information may not be available when needed, introducing time delays. An important example within the obesity system includes society’s emphasis on body weight and not health, providing misguided and sometimes wrong, information. Another example is that of variability and errors in self-reported measures, such as for physical activity levels and food consumption.

Figure 1: Double Loop Learning. Adapted from Sterman, J.D. Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill. Boston. 2000

Our mental models may also themselves be barriers. These mental models or deeply beliefs, exist at multiple levels: within the individual, within communities, within policies. Unscientific reasoning or misapplication of scientific reasoning, judgemental biases, and defensive routines are pervasive within the obesity system. Blaming the individual as cause, and solutions that look no further than the inappropriate recommendation to simply “eat less and move more” are common beliefs. Challenging these models is necessary for change.

The strategies, structure and decision rules and the decisions we make are also fraught with potential challenges, in part because they are based on our mental models. In obesity, the typical decisions focus on lifestyles changes (eat less, move more! or perhaps behaviour therapy), pharmacological solutions, or surgery. These are well intentioned scripts, but typically fail to account for the complex interactions of multiple factors that contribute to obesity as well as the heterogeneity of the population; only a subset of factors are germane for a given individual. These factors also change over time. But even the “right” or “best” decision that is not plagued by a biased mental model or incorrect information isn’t guaranteed to meet with success and can be subject to implementation failure. For example, the challenge of translating interventions developed through research into practical settings.

We can only attempt to simulate and understand the real world; our models cannot fully capture the dynamic complexities, experiments cannot be controlled, again introducing the potential for errors (to quote George Box, “all models are wrong, but some are useful“). The dynamic complexities of the obesity system are reflected in the multiple factors and their interactions found in the Foresight Obesity Map. Not only are there many factors, there are multiple connections, the relationships are non-linear, they exhibit stochastic behaviour, and include both negative and positive feedback. Yet despite this complexity, the interventions we most often try to implement are reductionist, assign blame, focus on outcome rather than process and ignore our capacities.

For each of these barriers to learning, the challenge lies in shifting our current beliefs to enable implemention of strategies that embrace a complex systems approach (e.g. matching complexity to capacity, establishing networks, fostering emergence, etc…). 1. Sterman, John D (1994). Learning in and about complex systems System Dynamics Review, 43 (2-3), 239-330 DOI: 10.1002/sdr.4260100214

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