BOOK PREFACE

Why would anyone want to predict people’s plans? There are several reasons.

Firstly, humankind faces many problems, ranging from international conflict and global warming, through crime prevention and drug rehabilitation, all the way down to what to wear today and how to eat tonight.

Secondly, although governments, companies and institutions pay professional planners handsomely to plan our way out of society’s problems, they cannot do this.  Humans are too mysterious, unreliable and perverse for any planner to be able to select a plan which is perfect, or even tolerable for everyone.

Thirdly, if planners could somehow predict which plans will be popular and adopted, and which plans will be unpopular and sabotaged, then they would at least have much more empathy with the planned for. They would plan much more sympathetically and so their success level would skyrocket.

So, in a nutshell, this book is predicated upon a few core beliefs:

1.   optimal planning is impossible
2.  plan prediction will bring us closer to it
3.  plan prediction is probably possible, but this needs to be scientifically tested.

As such, this text contrasts to most of the unscientific, strategic-planning literature. The latter tends to be strong on hubris, self-boosting and hyperbole. Very little of it is ever based upon a rigorous testing of the viability of what it says. 

Granted, some strategic planning has proved to be informative, interesting, inspiring and useful. Indeed, I have been impressed by many dedicated, talented and sincere researchers and practitioners with whom I have worked during my time as a planner within government, private enterprise and academia.

Yet it has to be said that most of the literature gives oxygen to the huge
deception that finding a “best” plan, one that suits everyone optimally, is actually possible. In fact, vast numbers of strategic planners around the world stake their careers, identities and future promotion prospects upon this fabrication being true. Massive salaries, consultancy remunerations, expert witness fees and advisory stipends hinge upon the myth that a “best” plan for everyone is actually discoverable.

Worse, most laypersons have accepted this myth, especially the version that is peddled by articulate “experts” who have spent their entire career exclusively within some specialized sector, be it farming, furniture, fishing, fashion or fenestration. Such aficionados always speak so authoritatively that it is surely they who have the clearest perception of what has happened in the past, what will happen in the future and which plan is best for everybody right now. They are handsomely rewarded and widely sought after, simply because they sound so seductively reassuring.

It is similar with academic researchers who explore human-built phenomena and/or people’s behaviour. Their findings seem to be continually expanding our insights to the point where good policy-making is just around the corner.  Accordingly, the taxpayer keeps paying for their research on the dubious assumption that it will equip planners to optimally tackle the daunting problems of modern civilization.

But whenever so-called experts tell you that they can formulate an optimal plan for everyone, they are kidding both you and themselves. If, at a cocktail party, practitioners of esoteric, social science research say their work will lead to optimal planning, they are simply overstating their importance. Perhaps this is due to their understandable hankering for making their work seem more practical than it really is.

I am not denying that some strategic planning research has sometimes generated good heuristics for avoiding the worst types of imminent catastrophe. It has constituted genuine future proofing within our turbulent, complex and fast-changing world. But it is a fact that ever since the first strategic planners began to ply their trade, there have been some monumental failures. Several were outlined in the late Peter Hall’s wonderful volume entitled Great Planning Disasters (1980), and others will be described below whenever it suits our purpose.

I will also discuss various alternative approaches to strategic planning, like that of Marxists who suggest optimal planning is simply a matter of fixing the pivotal problem — class inequality. Moreover, many environmental “designers” naively believe that the optimal plan for addressing complicated, modern problems somehow resides within their own, or their favourite guru’s innate creativity.

By contrast, I will argue that everyone should resist this siren call to pursue supposedly perfect planning. Instead, I prefer to aim at something far more modest— plan prediction, the subject of this book and an activity which, up until now, has been largely ignored.

Yet not everyone has neglected it. For example, the famous marathon runner Derek Clayton once claimed that he could run fast because he was able to imagine how those struggling along behind him must be suffering even more than he was.  His empathizing with his opponents’ agonies, self-doubts and probable coping strategies enabled him to anticipate what they would do next — a prime example of how plan prediction can be turned to one’s own advantage.

But alas!  More common is the tendency for most of us to lose games of table tennis or chess precisely because we fail to predict our opponent’s plans. Instead, we concentrate upon further improving our own plan-formulation skills in the vain hope that we will eventually win. By concentrating too much upon our own strategies and not enough upon predicting our opponents’ plans, we frequently snatch defeat from the jaws of victory.

Now, since book prefaces are convenient places for confessions, I hereby declare that in the past I have indulged in such self-centred planning myself. I even wrote two books explaining how to plan better (Wyatt 1989, 1999). Indeed, I once had a full time job as a hotel-location advisor for a large urban brewery. I was paid to build a mathematical, city-growth model for predicting the directions of urban expansion. Such prediction would guide the company towards where to locate new hotels before its corporate rivals did.

But again, because of humans’ unpredictability, no technique can ever predict the exact direction(s) of any city’s expansion. People are so capricious that their deeds seldom mirror what behavioural analysts predict. As has been said before, humans are more complex than our models of them are. So the whole city-growth-prediction exercise, despite bringing superficial prestige and other transitory, spinoff benefits to the brewing company, was something of a beat up.

Perhaps by way of atonement, therefore, this book is much less ambitious. It develops a usable plan-prediction method that can be implemented by anyone who plans within a socially sensitive or politically delicate environment, which is all of us. The method predicts what sorts of people will prefer which plan — in any situation.

How has it been developed? Perhaps ironically, it is based upon the considerations which, for 100 years or more, writers have been telling us people think about whenever they plan. It also exploits some of the methods that professional planners have used to throw light upon such parameters. So this book doubles as an anthology of planning methods, and by reading sequentially through it, you will gain a working understanding of the different planning approaches’ strengths and weaknesses.

But our underlying purpose is to develop an accurate, plan-prediction system, and Chap. 7 duly delivers it in the form of an “app”. Those interested only in the method, therefore, might want to read Chap. 7 first, and then only dip into other parts of the book for further illumination of the app’s characteristics.

These characteristics, to cryptically summarize again, stem from the observation that people, to varying extents, prefer plans which are:

1.   practical—safe, easy and fast.
2.  productive—effective, efficient and timely, and
3.  prudent—acceptable, permissive and independent.

That is, our plan-prediction method involves rating alternative plans for practicality, productivity and prudence, as well as for safety, ease, speed, effectiveness and so on. Then, on the basis of how different kinds of people view the relationships between plans’ scores for these criteria and their overall desirability levels, it predicts who will see what plan as the most desirable.

Hence this book falls naturally into three parts containing three chapters each.
Part I, “Common Sense Plan Prediction”, describes real-world influences acting upon people whenever they plan. Part II, “Plan-Prediction Parameters” operationalizes such influences by teasing out from the literature some criteria for accurate plan-score forecasting. Finally, Part III, “A Plan-Prediction System”, describes our method and how it has fared in practice.

More specifically, the first three chapters below respectively outline the
importance of practicality, productivity and prudence when predicting people’s plans. The middle three chapters then respectively break down these three criteria into more detailed parameters, and the last three chapters present, in turn, our Plan Prediction app, the nature of people’s responses to it and its growing usefulness as a research tool.

But our plan-prediction method, which is actually a new form of Decision
Support System (DSS), has a deeper agenda. It is, at heart, an attempt to put some much-needed scientific rigour into the DSS discipline. It constitutes a giant experiment to see whether or not we really can predict people’s plans accurately, and to this end it self monitors via fairly straight forward, statistical methods. It automatically, continuously, clearly and unambiguously tests how accurate its own plan predictions are.

Our app can be run on computers or smart ‘phones by users everywhere. This means that, users can “teach” it to become more and more accurate as more and more people use it — it is, theoretically, self-improving. Whether or not it improves in practice can, of course, be tested by observing whether or not its forecasts’ error margins decrease, and/or there is an increase in the testable probability that its predictions are correct.

In other words, we here present a DSS which, perhaps unlike any other, does not insist that its predictions be accepted on faith alone. Instead, it self-tests in an entirely transparent way. Preliminary investigations with earlier versions suggest that its predictions are accurate at least 70 % of the time, but only additional users will allow us to see whether or not it learns to achieve greater levels of accuracy than this.

It is important to realize that if good plan prediction really does prove to be possible we will have achieved it for the first time. Disciplines like Psychology, Behavioural Economics, Cognitive Science, Philosophy and Environmental Design have all made attempts, but none has come up with a workable plan-prediction method. Either they have generated vague principles that are of little practical use, or they have formulated very exact predictions which only work within research laboratories rather than in the real world.

I am fairly confident that our method will be a success because it has some powerful and unusual features. One of these is its avoidance of a perennial problem that has always dogged conventional multi-criteria decision-making (MCDM) — how to assign importance weights to plan-evaluation criteria. Such weights gradually evolve naturally from our app’s aggregating and, hopefully, increasingly precise, group-specific relationships between criterion scores and plan desirability.

Another important feature is our app’s determination not to become a mysterious “black box”. It goes to great lengths to be fully explanatory. It uses my original and innovative “face charts” method in an attempt to succinctly clarify why different sorts of people are likely to prefer different plans.

Such attempted explanations have always generated productive discussion at the several stakeholder workshops, underpinned by our method, which others and I have conducted over the years. Workshop participants usually progress quickly to the stage of speculating about what modifications to mooted plans might be necessary in order to make them more palatable to certain kinds of people. So, even if it transpires that our method is less accurate than anticipated, our app still functions, right now, as a useful circuit breaker and insight generator for focus groups.

This book is narrated through the eyes of two fictional characters. We trace through their respective life journeys and their frequently fraught attempts to uncover people’s key plan-evaluation criteria on which to base a viable, plan-score forecasting method. Such humanization is an attempt to make the book more readable and plausible, although all assertions are still supported with references wherever possible. Moreover, our two characters’ markedly different personalities serve to emphasize how accurate plan prediction needs to be both quantitative and qualitative. Plan prediction is both a science and an art.

If you truly empathize with our two characters’ beliefs, desires, hopes, fears, aims and achievements, you will think deeply about the nature of plan prediction. And the exercises at the end of each chapter should make you think even more. At minimum, they will prompt extensive class discussions in courses where this book is obligatory reading.

Finally, understanding the text below does NOT require any pre-knowledge of difficult philosophical, mathematical and computerized concepts, despite some of the latter being discussed both profoundly and incisively. This book has been written for less technical, but mature and intelligent readers—senior undergraduates, postgraduates, savvy practitioners and curious laypersons.

Enjoy.