I. Systems, Models and Frameworks
The remainder of the Guide will focus primarily on organizational foresight, the resources and tools that help us do better foresight with other people, on teams and in organizations. But many of these models can also improve our personal foresight as well, as they help us make better professional and life decisions.
The foundation of organizational foresight is a good understanding of systems, models, and frameworks that appear to operate in the world, and to effectively describe or manage things that professionals care about. This chapter could just as easily have been called Systems. We can consider it an implicit title. Systems thinking and systems theory are foundations of foresight, and model-making is the core of systems analysis. Many useful models (simple simulations of complex systems), and frameworks (guidelines for doing professional work) have been developed in the last sixty years of foresight practice. We constantly use such tools (referred to together as “models” in this guide) to make sense of the world. Thus several models in this chapter are relevant to all four of the UPGO practice domains, but we’ll keep most of our attention on their implications for organizational practice.
Whether they are unconscious or conscious, implicit or explicit, informal or formal, our systems models are the foundations on which we build our practice. They are our mental “maps of what matters.” Models are thus also dangerous, as they can bias us to prefer certain foresight methods (specialized practices to produce foresight, seen in the next chapter) over others, and they limit what we pay attention to, or even see.
For some, this chapter is the most challenging in our Guide, and may be better skimmed than read. There are a dizzying variety of models that can help you make sense of your client’s problems, pick better foresight methods and balance and prioritize your work. This is only a good sample. Knowing which is the best fit for your needs isn’t always obvious, and comes with experience. Reviewing the literature advocating each model to find case studies, and judging whether those are similar to yours can be very helpful. But the first step is to be aware of the great breadth of models that have proven helpful in foresight work, so let’s begin.
Models We’ll Consider in this Chapter
We’ll now look at some good foresight models, in two main classes: I. Category Models, which propose key categories for foresight thinking and action, and II and III. Process Models, which look at processes which flow between categories. In Process Models we will look mainly at cyclic models, those that propose a cyclic flow between categories. We do this in homage to the evo devo theory of change, itself a cyclic model. If we live in an evo devo universe, cyclic flows, both evolutionary and developmental, are going to be the most interesting and important processes around.
We begin Process Models with II. Decision Cycle Models, loops aimed at producing more foresighted decisions and actions, both personally and in organizations. Here we will introduce the Eight Skills of Adaptive Foresight, a cyclic practice model that fits the Toffler-Amara Three Ps of Foresight model and Gallup’s Leadership Domains model into the Shewhart-Deming Learn-See-Do-Review decision cycle. Given the Eight Skills grounding in these three broadly valuable models, we think they are a particularly complete and powerful approach to foresight practice.
The Eight Skills are so central to building Adaptive Foresight, in our view, that we use them to organize the various Foresight Methods professionals can employ with their clients, which we turn to in Chapter 10. So we suggest paying particular attention to this model.
Our next set of process models are introduced in III. Change Curves and Other Cycle Models, where we’ll review patterns of change that are common to a variety of complex systems relevant to foresight work.
In IV. Frameworks, we’ll present a starter set of process recipes for producing useful foresight work. Frameworks are considered by their authors to be “recipes”, not models. The “chefs” that have developed these models encourage you to experiment with them, and vary the recipe yourself, to your taste. As with any recipe you may find more or less of certain steps, or the addition or subtraction of steps, to improve the outcome with particular clients.
We hope these models and recipes inspire you to find, adapt, build, and share your own in your foresight work.
The more theory-grounded and evidence-based foresight models and frameworks you know and can use, the more versatile you will be in deciding which to adopt or adapt for particular projects or clients. Knowing a variety of good models will also help you to learn and prioritize the even greater variety of methods on offer.
Good models offer us many potential benefits. They can be:
Consensus-promoting. We learn a common language and outlook on complex systems.
Prioritizing. We focus and balance our attention on what seems to matter most.
Clarifying. We see relationships, processes, and agendas we may previously have overlooked.
Efficient. A good model helps us to quickly make sense of a complex, ambiguous system.
Quantitative. With good definitions, our models can be numerate, and more precise.
Validated. Good models can be replicated, & pass statistical tests of significance & predictability.
Models have many potential drawbacks as well. They can be:
Biased. The bias or agendas of the designers may skew the model/framework far from reality.
Incomplete. The model can lack key datasets in its development, and be insufficiently diverse.
Over/Under Complex. The model can miss key system elements, or have more than needed.
Incorrect. The model may have incorrect categories or causal relationships
Vague. The model can lack sufficient rigor to be subject to quantitative tests.
Imperfect as they are, models are helpful as sense-making tools and as easy shortcuts (sometimes too easy!) for strategic thinking. Finding, choosing, and occasionally building the right model for your various foresight clients, contexts, and problems is both an art and a science. Systems thinking and model building are great first steps in seeking to understand an organizational system or problem, and this activity can help you devise better processes to manage or change the system.
Sometimes, quick qualitative modeling is all we can do with the time and money available to us. Peter Checkland’s Soft Systems Methodology, developed in the 1960s, uses consensus-seeking and systems thinking to build and validate qualitative models for organizational problems. See Brian Wilson’s Soft Systems Methodology (2001) for a primer.
On the more quantitative side, the Entity-Relationship community is a group of information professionals who make tools to abstract complex systems into sets of Entities, Relationships, and Constraints. In the 1990s they developed a Unified Modeling Language for software engineering, and have held an annual conference on conceptual modeling since 1979. As our software slowly gets better at machine learning sets of entities, relationships, and constraints from the data around us, and using collective human judgment to tune them, the future of conceptual modeling looks bright.
Making our models explicit makes them subject to critique and improvement. Prioritizing the models we find most useful also helps us clarify our assumptions and values. This chapter will introduce a small selection of foresight models and frameworks that seem particularly valuable.
All models must be both partially incorrect and perennially incomplete, but we hope you find value in the ones we have selected. Please let us know where you think any of these, or more likely my explanations of them, fall short.
Which classic models and frameworks have we missed? Let us know for the next version of the Guide.