Voyage into Chaos

One challenge for positive psychology is in providing solutions that generalize or have universal effects (particularly given its scope across the population). However, there is some evidence that not everyone benefits from the same intervention (Lyubomirsky et al., 2011). Indeed, our own work in primary schools using interventions such as Three Good Things (Shanks and Parkinson, in prep) suggests that already happy children might actually show a decline in happiness if they are forced into reflecting on their lives. So there may not be a ‘best practice’ that applies to everyone or in all contexts. Understanding this complexity is a major challenge for research design and acknowledges the multitude of factors that contribute to individual, social, workplace and community environments. One approach to understanding such complexity of knowledge is that of the Cynefin model (Figure 1) developed by David Snowden (e.g. Snowden, 2005).

Figure 1. The Cynefin model of knowledge. From Snowden, D. (2013) Cynefin framework, http://en.wikipedia.org/wiki/File:Cynefin_ framework_Feb_2011.jpeg

In WCBC terms, we want to identify the best positive psychology intervention for a given situation and Cynefin is a framework that helps us understand the landscape and set off in the right direction. For example, when solving a math problem – a Simple domain, depicted bottom right in Figure 1 – there are clear constraints and rules on information and knowledge. Those rules help to navigate solutions and there is an absolute correct answer. By contrast, in Chaotic domains such as weather forecasting (bottom left, Figure 1), there are very few clear rules, much ambiguity and uncertainty. In some geographic regions it is almost impossible to make reliable predictions beyond a few hours. Between these two are Complex and Complicated domains which reflect the conflict between order and chaos. Interestingly, the right half of the framework (Simple and Complicated domains in Figure 1.) conforms to rational, knowable, explicit information whilst the left half reflects knowledge that can only be understood in intuitive and implicit ways. One implication of this model is that trying to understand, and to navigate, complex environments can only be realistically achieved by engaging our own intuitive, implicit processes. So there is self-similarity at multiple levels here: behavioral intervention technologies need to target the implicit system of our audience to facilitate ‘good’ behavior, and at the same time, the researchers designing those interventions may identify best-fit solutions by also engaging those processes. It should also be apparent that many situations in which individuals struggle to behave as they intend (the value-action gap) conform to those situations which would be categorized as Complex in the Cynefin framework.  

According to Snowden (2005) individuals and organizations often work on the belief that chaotic behavior is seen as ‘the antithesis of order: total and absolute turbulence, without form and substance’, and so undesirable. By and large, organizations will attempt to limit themselves to operation within the Simple and Complicated domains (right side of the figure), where application of existing knowledge can be controlled and leads to best (or at least good) practice. However, the complex environments of the workplace (or social the landscape of communities) are rarely Simple or Complicated. Most often they are truly Complex and at some times totally chaotic. How do we design positive psychology interventions that will achieve their desired effect? Whilst there are difficulties in working in complex domains, doing so can be valuable as they allow for emergent and novel knowledge creation – innovation and new solutions. What follows is that in order to generate effective behavioral research designs in complex situations, it may be useful for the research team to engage their intuitive experiential brain systems in order to find novel emergent solutions and innovations. 

Communication for Inspiration

One acknowledged route to innovation (in our case innovative behavioral solutions tailored to the needs of the end-user) is through increased communication. Specifically communication across specialist domains. Many examples of innovation come from solutions that have been taken from one specialist domain and applied in a novel one. For example, Google’s search function currently dominates the internet as the engine of choice as well as driving the way organizations design their online presence and marketing. The innovation in its functionality came about by taking a well-known solution in one domain – that of preferentially ranking data in a manner that is of value to the user – and applying in another (internet information search). Serendipitous innovation can be promoted by bringing specialists together who would not otherwise talk. This communication for inspiration can to some extent be managed through design of the organizational and physical structure (Allen and Henn, 20007). In terms of physical space, Allen and Henn give the example of a monastery: there are cells for the monks (the specialists) to concentrate and apply their expertise. And then there is the cloister where the monks mingle and interact with others (specialists from different domains) sharing ideas and having inspiring conversations. Traditionally, universities have tended to put their specialists in silos (departments) with little opportunity for cross-fertilization across academic domains. (Though the collegiate system at institutions such as Cambridge and Oxford are a perfect example of using organizational and physical space to promote communication for inspiration.) In recent years, some research laboratories have designed for communication by, for example, placing academic offices around a central communication area – usually with a coffee machine (for example, the MIT Sloane School of Management, as described in Allen and Henn, page 18-19). Bangor University is in the process of developing a new physical space – the Innovation Quarter at Bangor University (IQ@BU) which will be designed with these considerations in mind. The WCBC will reside within this greater space.

Without the luxury of manipulating the physical space, teams can still be created with the aim of promoting communication for inspiration, leading to innovative solutions. This usually takes the form of taking specialists out of their departments and building multidisciplinary project teams, and this is how we designed the organization of the WCBC. On the one hand, we might have taken the traditional approach to creating a research center – in this case focusing on behavior – by simply employing a team of behavioral psychologists to consider the various applied briefs and developing solutions from their specialist background and knowledge. Instead, we designed the center with communication for inspiration in mind and employed a multidisciplinary team to work together on creating solutions. This multidisciplinary approach is one that current UK funding councils are also enthusiastic about. Indeed, some grant funds now require the establishment of multi-center teams (e.g. the UK Medical Research Council) in order to apply.

Figure 2. The MUD(BASE) prototypology (Goodman et al., 2013), which underlies the multidisciplinary structure and process followed by the Wales Centre for Behaviour Change. Each petal represents a specialist domain and the team as a whole functions to use a design thinking approach to developing positive behavioral solutions through multidisciplinary innovation. This organizational structure is specifically designed to provide a balanced and creative contention among specialist collaborators. The value ultimately created by meeting user-requirements, defined at the center of the structure, exceeds that created by the BASE specialists working independently.

The structure of the WCBC is based on an innovation DNA designed by an academic at Bangor University (Goodman, in prep), which enables multidisciplinary user design of solutions based on the combined expertise of Arts and Science (the Da Vinci axis) as well as Engineering and Business (an entrepreneurial axis), with the design process at the core (forthwith the muD.BASE structure). The WCBC employs academics from these specialisms: psychologists (Science), engineering (Engineer), digital humanities and creative media (Arts) as well as user-experience design (Design). The business (Business) element of the muD.BASE comes from the clients that the WCBC engages with in identifying behavioral challenges. The team then employs design thinking (see Cross, 2011) to rapidly shape and prototype research methodologies. The end-user is included in this co-design, so the research results in solutions that are much closer to implementation than is normally the case (Implementation Science; Eccles et al., 2009). 

Thinking by Design

The design thinking process differs somewhat from a standard academic approach to research design (Cross, 2011). It actively promotes intuitive, freeform brainstorming in a divergent manner and initially focuses on the desired end-point to shape a vision (see Figure 3.). A subsequent stage of filtering or ranking helps to identify ‘best’ design methodologies for the given goal. The path to the goal is then achieved through iterative prototyping and approximations (Buchanan, 1992). Whilst first employed in engineering it is now used across domains particularly in innovation, commercialization and business. Essentially, the power comes from harnessing an experiential process that engages the implicit decision-making mechanisms of the brain (Kolb, 1984; Damasio; 1994). 

Figure 3. The design thinking approach to developing prototypes and solutions. It works through an iterative process of focusing on the problem and solutions spaces; ideating in a divergent and creative way; ranking and converging on best-fit solutions; and executing the plan to generate evidence to support further prototyping, shaping and design iterations.

Selection of ‘best fit’ solutions is achieved through ranking and the application of rational criteria and then evidence is acquired through pilot research and prototyping to feed in to the next stage of research development. Overall, the WCBC’s approach to research design can be captured as intuition driving research, evidence driving policy. With multidisciplinary experts in the team, there is a significant breadth and depth of implicit knowledge available to the design process. The process perhaps works best where there is some existing evidence in the literature to help constraint design methodology and implementation, but sufficient uncertainty about best practice to harness the power of the design thinking process (the interface between Complex and Complicated domains in the Cynefin knowledge space). The ultimate output is an evidence base of what works in different implementation environments.

 

References

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