Tunisia is part of a first cohort of UNDP Country Offices involved in a comprehensive exercise of designing a development portfolio, one of development options. The principle focus of this portfolio is Trust around public institutions in Tunisia. The exercise is termed ‘Deep Demonstration’ and just like the term suggests, it is a thorough process of making sense of Trust around public institutions in Tunisia and creating a series of programmatic artifacts which embed the learnings and insights generated and aim at providing a structure for knowledge building and future interventions in Tunisia. With the technical support of @chorafoundation, the deep demo team in Tunisia has elaborated a series of programmatic artifacts. Throughout this blog, I will discuss how certain characteristics of these artifacts and how they can help us generate robust findings about development challenges.
Nature and purpose of Artifacts being designed
The nature of the Deep Demo Artifacts
A problem space is a visual representation of a learning and intervention logic. We mean by this, that the problem space allows portfolio developers and users to demonstrate how intelligence about a topic, in the case of Tunis, Trust, can be organized and provides a tentative logic of intervention (see figure 1). This programmatic artifact provides a common language to allow for a meaningful dialogue across multiple stakeholders.
Figure 1: Source: @Chorafoundation
A portfolio intent is a statement clarifying the objectives set out by the portfolio in terms of knowledge building (learning) and potential logic of intervention. This particular artifact sets out the scope of your portfolio and helps explain what it is exactly we are addressing in development (See figure 2).
The purpose of the Artifacts being designed
Are we designing perfect programmatic artifacts which embed, in an ideal way, the nature and complexity of the relationships between building blocks and social constructs of Trust in Tunisia? The short answer is no, we are not. The representation of a problem space provides a broader structure to knowledge building and shapes up an eventual logic of intervention around the notion of Trust. The main purpose of the deep demo project is to provide an actionable learning artifact to instruct potential future interventions and further learning activities. This artifact need not be perfect but is required to maintain a significant level of sensitivity and adaptability to emergent and emerging knowledge and learnings. The greater ambition of the process is the design of development interventions and policies stemmed from a deep knowledge of systemic constructs of the reality of trust and how these relate to relevant development challenges.
Why specific characteristics can foster complementary and robust intelligence on development challenges and design more adequate interventions?
Five characteristics of the artifacts produced among many will be addressed and appear most relevant to the learning question set out: Emergence, Participation, Uncertainty, failure-tolerance and built-in feedback mechanisms.
Shaped by emergence
Emergence in social sciences research refers to any findings or variables which results from initial analysis of raw data and which can often direct the subsequent stages of analysis. This is different from research designs where measured variables are pre-determined based on theory or observation-driven hypotheses. The latter is done in a logic of supporting or rejecting existing frameworks while the former is more exploratory in nature in that it searches for new patterns emerging from an ocean of data. This is usually more adequate when our understanding of the subject is limited, no previous theoretical frameworks have been established or suffice in understanding the complexity of the subject at stake.
We already argued that trust is a social construct shaped by many forces, many of which remain obscure to social scientists. Indeed, if trust was governed by a clear mathematical model, then we would have seen an era where politicians, governments and business owners play on very specific variables to evoke trust from the public. But while we are aware of some aspects of trust dynamics, many remain a mystery. In this case, looking from emergent patterns can bring new insights to our understanding on the subject. It is important, nonetheless, to note that while unveiling new patterns, we are still very much biased by existing heuristics. Which is why the next section covers an important aspect of sense-making: the collective.
Shaped by the collective
Exploratory missions in Archeology benefit a lot from numbers. While only a few experts determine the scope and exact area for a dig, you’ll need as many hands as possible to increase your chances of finding something valuable in the process. The more objects you unveil individually, the more you’ll learn about an ancient civilization, its rites, its tools, or beliefs. When we dig into a data sandpit, we need many people to make sense of the data without falling into individual biases. When insights on trust emerged from the interview phase of the deep demo project, like ancient obscure objects, we had to draw from a collective well of theories, existing knowledge and previous observations to fond emerging patterns, gradually abstract raw data to shape and re-shape our programmatic artifacts: the intent and problem-space of the Trust portfolio. This participatory process does not control for biases but instead shapes a collective experiential programmatic artifact.
Imperfect fit: uncertain solutions to an uncertain understanding of the world
If we are to adopt an earnest posture in addressing wicked development challenges then we must first admit that our understanding of the forces governing these challenges are imperfect, (incomplete, biased, prone to error) and uncertain (shaped by individual and collective assumptions, applied to evolving subjects).
Following the same logic, solutions designed can only take similar characteristics of imperfection and uncertainty. It is only by stating the uncertain and imperfect nature of our programmatic artifacts that we can treat them as prototypes designed to be tested and re-shaped or redesigned. The difference here is that prototypes, classically, both in engineering and design, aim for a perfect fit, eventually, but what if our final artifact does not aim for perfection or is not supposed to adopt a ‘final’ form at all? Keith Grint makes the case for “clumsy solutions”. According to him, “to get some purchase on Wicked Problems we need to start by accepting that imperfection and making do with what is available is not just the best way forward but the only way forward.
Trust, has the characteristics of a wicked problem, just like many other problems addressed in human development. Wicked problems are perceived as complex either because they are intrinsically so due to complex relationships between various factors and components and their non-static nature or simply because of our perception and limited comprehension of the way different elements governing the problem function. Either way, we are dealing with uncertainty. Uncertain and evolving systems with an uncertain understanding of how these systems work. But how do we design for uncertainty? Michail Chester and Costa Samaras plead for “Loose-fit infrastructure”, designed to be future-proof in a world where climate change is uncertain and unpredictable and keeps redefining the parameters of infrastructure. These ‘loose-fit’ solutions capitalize on “agility and flexibility, assets that are modular, multi-functional, and scalable (up and down).” The aim of the deep-demo programmatic artifact is to be robust in the face of complex, ever-evolving systems, and an uncertain understanding how they work.
The crucial point is to have built-in mechanisms to allow our problem space and portfolio-structure to evolve based on the learnings it generates.
Failure and the generation of intelligence
We demonstrated during the previous argument that imperfect or “loose fits” can make for better solutions when we do not entirely understand a challenge we are addressing. Imperfect solutions can also imply a given degree of failure. What Grint refers to as “clumsy solutions” or Chester and Samaras as “loose fits” take a humble posture where mistakes are permissible. This is the exact opposite of solutions which are designed to have a minimal number of flaws thanks to an extensive analysis and planning process.
Question is now, why should we not shy away from designing imperfect systems, ones prone to error? In an article named “The wisdom of deliberate mistakes”, Gunther and Shoemaker present a very compelling argument in the favor of ‘failure’.
“making mistakes--correctly--is a powerful way to accelerate learning and increase competitiveness.”
For Gunther and Shoemaker, testing an erroneous assumption can generate evidence faster and build more accurate intelligence for a company than the process of “considering only data that support the company’s assumption”. Situation analyses in development often look at data which supports initial assumptions, which stem either from situations in different spatial or temporal contexts. In this sense, programmatic artifacts built to correctly test viability of erroneous assumptions can generate very rich data. A very compelling example used in the latter paper is a company who chose to not to take security deposits from customers they consider as ‘high risk’ according to their own screening system. They then analyzed payment behaviors within this sample, only to find out their initial assumptions about screening criteria turned out to be wrong. This helped them design a better screening system.
Deep demonstration programmatic artifacts such as the problem space or areas of interest (figure 3) present a set of assumptions resulting from a long process of making sense of many insights gathered throughout the data collection phase. We can call them evidence-based assumptions. Tested, these assumptions, which may turn out to be erroneous can generate quick intelligence to help us understand the problem at stake: Trust paradigms in Tunisia. The quicker we fail, the quicker we learn.
Deep demo programmatic artifacts as boundary objects
We already talked about eternal prototypes; ones designed for “clumsiness” instead of perfection. Let us take another important use of prototypes, in this case our portfolio intent, problem space and areas of interest. Let us consider how prototypes evoke different forms of feedback to better understand user needs. Users are often not capable of expressing their needs when the product desired is novel and thus only theoretical in nature. Creating a shared model can prompt users to better express their requirements through feedback (Rhinow, Köppen, and Meinel, 2012). Following this logic, a prototyped problem space can prompt various stakeholders to generate concrete feedback to feed into a more relevant logic of intervention on trust in Tunisia and support knowledge creation through the development of a common understanding of the problem.
A dynamic and flexible portfolio built on the principles of uncertainty, failure and collective subjectivity can only be perceived as an atypical programmatic artefact. But what if these characteristics were the very strengths of a new learning paradigm, one where built-in mechanisms of probing, feedback-priming, and learning from failure and inadequacy can help us gather powerful intelligence and design more flexible and future-proof development models? More importantly, and as I have argued throughout this blog, these artifacts allow for faster learning with more room allocated for experimentation learning and revision/re-design. Deep-demo artifacts such as the problem space or areas of interest might be exactly what we need in an uncertain and complex environment.
Chapter 2 will address the challenges related to the socialization of these programmatic artefacts.
Holger Rhinow, Eva Köppen, and Christoph Meinel: Prototypes as Boundary Objects in Innovation Processes. Conference Paper in the Proceedings of the 2012 International Conference on Design Research Society (DRS 2012), Bangkok, Thailand, July 2012
Grint, Keith. "Wicked problems and clumsy solutions: the role of leadership." The new public leadership challenge. Palgrave Macmillan, London, 2010. 169-186.
Schoemaker, Paul J., and Robert E. Gunther. "The wisdom of deliberate mistakes." Harvard Business Review 84.6 (2006): 108-15.
Mikhail Chester & Costa Samaras, “Loose-fit infrastructure can better account for climate change”. thehill.com, April 2021