Choiceboxing: An Introduction
Most of us know someone who has gotten stuck on an idea. They’re convinced that the idea is not just original and important, but transformative. They find it endlessly fascinating. They can’t give up, even after years of trying.
Such people seem obsessed. They may burn thousands of hours without any realistic prospect of achieving anything. They may feel that the idea has chosen them. It’s the monkey on their back.
I’m one of those people.
Many other ideas and interests have haunted me. (Jazz, experiential education, legal knowledge automation, Shakespeare authorship, …) But one has long felt different. It revolves around an activity dubbed “choiceboxing.” This article is an introduction to my obsession. It includes handy links to most of the places I’ve written and talked about it.
The big idea
People face choices throughout their personal and business lives. Some are nearly invisible and instantaneous; others involve extended deliberation and debate. Some are made by one person alone; many involve consultation with others. By many accounts they are becoming more frequent and complex. We deal with choices all the time, and few of us are very good at them.
Most people don’t use tools to support decision making. That has something to do with how unreflective we tend to be about our deliberations. We are woefully unsystematic when it comes to decisions, despite their being among our most pervasive and consequential activities.
I had a series of epiphanies starting in 2003 about using interactive visualization to help make important choices. And have been thinking, writing, and talking about that ever since.
A lot of the basic ideas and their potential uses are covered in the below sections. They’re also in the Choosing Smarter chapter of my book. If you’d rather watch than read, here is a 13 minute overview I gave at a 2016 gathering of chief information and knowledge officers at the White & Case law firm in New York City. For a shorter intro, see this 7 minute screencast of choiceboxing basics.
Two quick points first:
· Many decisions require us to wrestle with uncertainty, where the likelihood of various options and outcomes is a central concern, and we have to engage in forms of probabilistic reasoning that humans are so bad at. Choiceboxing is more focused on the separate challenge of managing preferences across competing considerations, which often remains even when there is little or no factual uncertainty about the alternatives and consequences.
· There’s of course a vast literature on choice making, and a venerable discipline of decision science. I’ve consumed a lot of the literature, but remain a rank amateur and outsider. (That’s part of what makes me think I might be on to something.)
Basics
Choiceboxing uses an interactive three dimensional representation of a decision in process, in which all options, factors, and evaluative perspectives can be accommodated. It can be used for individual decisions, or as a kind of Wikipedia of Choice whereby collective wisdom can be accumulated and shared.
By convention, options are positioned left to right, factors top to bottom, and perspectives front to back. There is a column for each option, a row for each factor, and a layer for each perspective. Each cell at the intersection of such a column, row, and layer represents the characterization of some option in terms of some factor according to some perspective.
Choiceboxing is best used within a systematic process of:
· Identifying aspects of candidate options that you might care about, with special attention to those aspects that involve true ‘make or break’ requirements;
· Casting a wide net for candidates, while focusing on those that satisfy essential requirements;
· Specifying how much you care about candidate features (organizing them in at least relative importance, so you can deal meaningfully with the inevitable tradeoffs);
· Assessing candidates on the important features, in a way that permits each to be given a roughly quantitative score on its relative goodness for each feature;
· Using a weighted scores approach to judge which candidate does best, all things considered;
· Iterating this process through group discussion and continuing deliberation.
An example may make this clearer.
Example: choosing a technology
Choiceboxing is most naturally applied to decisions that involve selections from discrete sets of options, like the choice of a particular software product.
Imagine that Jane and John work in an organization that is deciding which contract management system to buy. They’ve narrowed it down to three products: Ace, Acme, and Apex. After much discussion, the choice seems to hinge on three factors: completeness of features, quality of interface, and ease of learning. The following figure depicts how this matrix of options, factors, and perspectives might be represented in a choicebox. We’re seeing Jane’s perspective up front. The factors are matters of opinion, so her ratings and those of John may well differ.
The next figure makes the separate perspective layers clearer. Now we can see some of John’s different ratings, as well as average ratings on the combined layer. (The box can also usefully be sliced along the other axes, showing one option or factor at a time.)
Taking this a couple of steps further, one can express each assessment of each option from each perspective in a separate block of goodness like this:
One can then position such blocks within the overall framework of a choicebox, with associated totals, as follows. Note that the each person can set different relative importance heights for the rows.
You can imagine the total boxes at top as having been formed by melting down, combining, and reshaping the ingots of goodness in the columns beneath them.
Many real-world choices, of course, involve more options and considerations. Usually, though, the ultimate choice boils down to a small subset of each.
Preference functions, circumstances, and choice spaces. Oh my.
An important aspect of choiceboxing not shown above is the use of preference functions to map between possible features within given factors and their relative acceptability or goodness. Often option features are non-numeric (like the hand feel of a camera or the color of a car), and need to be quantified in order to be combined with other features. Even numeric features (like pixels or miles per gallon) may have non-linear implications in terms of preference. (Twice as many pixels may not be considered twice as desirable.) Moreover, the ranges of possible preferences for all factors need to be normalized to a common scale so as to be fairly commensurable in proportion to the explicit weights given by a decision maker.
The choicebox model goes on to take into account the circumstances of the parties and other facts that have a bearing on the relative goodness of options on different factors from the various perspectives engaged in a decision. For a ‘field guide’ to the myriad ways in which one choice can differ from another see part III of my Centrality of Choice article.
Another important concept is that of a choice space. That is the virtual space in which a number of decisions and decision makers — along with suppliers, helpers, and other would-be influencers — are in play. Such environments can yield powerful network effects. Choiceboxing is well suited for collaborative deliberation. It can leverage social production techniques to accumulate shareable knowledge as a free side effect of usage. My inventor’s notebook is bursting with ideas that build on all of this.
Multi-factor reasoning
Many of life’s situations present large sets of mutually independent dimensions, within which there are ranges we like and dislike. Consider the things most of us care about with respect to where we sleep:
· Warm, but not too warm
· Soft, but not too soft
· Dry, but not too dry
· Dark, but not too dark
· Quiet, but not too quiet
· Level, not sloped
· Stationary, not moving or vibrating
· Pleasant (or at least not unpleasant) smelling
· Companion(s) or not
We tend not to think much about most of these aspects except when something is not ‘right.’ Some are not always within our control. But we are willing to trade off optimal states in some dimensions for acceptable ones in others. And we recognize that folks differ in their preferences and their flexibility in managing them.
Applications
In addition to product selection processes outlined above, the machinery of choiceboxing can help in a wide variety of decision processes. It can be used to compare candidates for a job, arguments that should be emphasized in a legal argument, treatment options for a disease, or hypotheses about the cause of a machine malfunction.
In personal life, choiceboxing can help families choose a place to live, a vacation destination, or a nursing home for grandma. In business, choiceboxing can help organizations choose trading partners, negotiate agreements, and resolve disputes. Each of these contexts involves alternatives as to which there are competing tradeoffs and multiple evaluative perspectives.
Because I spend most of my time at the intersections of information technology and law, many of the examples I’ve used relate to one or both of those fields. But frankly I’m most interested in applications outside of law, where I suspect a billion dollar opportunity lies.
Legal applications
In law, choiceboxing can be used for client counseling (e.g. deciding whether to treat a staff member as an employee or contractor), legal services for the poor, and judicial decision making. It was used by a group of lawyers, judges, professors, and technologists to prioritize potential legal technology investments by the federal Legal Services Corporation. (Case study here.) The views of thirty-two participants on the relative efficacy of ten possible activities in achieving eleven desired objectives were elicited and consolidated in a structure that looks like this:
A two-part article that applies some of these ideas to contracting can be found here and here.
Here’s an extended discussion of how choiceboxing can assist with dispute resolution: Boxing choices for better dispute resolution.
As a former poverty lawyer and clinical teacher I’ve long devoted substantial attention to tools and services that address the legal needs of folks who can’t afford to pay for assistance. This piece in the Harvard Journal of Law & Technology lays out ideas about a ‘decision space’ that could support clients and advocates alike in nonprofit legal services contexts.
Educational uses
I’ve delivered six iterations of a Suffolk University course on decision making in which students create software apps for credit. Some use Choiceboxer. A core idea is that ‘cognitive prostheses’ — externalized representations of choices in progress that function as intelligent, shareable, virtual devices — can enable better choices for lawyers and clients alike. The course is described in The Centrality of Choice in Legal Work.
The company
Driven by enthusiasm for my favorite idea I started a company, called All About Choice (‘AAC’). We’ve secured a US patent, were counseled by the MIT Venture Mentoring Service, and have landed a few paid engagements, but have yet to achieve meaningful traction. My co-founder Andy Brewster and I have developed multiple prototypes and examined dozens of related approaches and initiatives. But we’ve also been distracted by busy day jobs.
The following is more aspirational than presently descriptive.
AAC builds online systems to help people make better personal and business choices. Its focus is on open and collaborative environments, radically simplified for non-specialists, backed by knowledge bases that learn as they are used. The goal is to make effective choice making widely available through intuitive technologies that leverage collective wisdom. Our company seeks to deliver a suite of choice support services that can be used by anyone anywhere at any time. We want to provide the very best solutions for choice making, and to serve as steward for robust communities of collaborating decision makers.
AAC has chosen to tackle hard problems in both user interface and back-end knowledge processing. Its initial objectives are to validate assumptions and confirm the feasibility of several key components. These include a Web-based application that makes weighted factor analysis compellingly easy, a dynamic ontology that captures evolving correlations of decision contexts and considerations, and tools that help manage semantic heterogeneity within and across domains.
Intelligent online decision support environments have significant potential. Vendors and consumers alike are greatly benefited when goods, services, and plans of action are effectively matched to authentic preferences. Heavy costs flow from inadequately informed or examined decisions. A powerful infrastructure for structured collaboration among the deciders, providers, and advisers active in most choices will require both cutting edge technology and business innovation, but yield high payoffs when achieved.
AAC operates at the intersection of artificial intelligence and intelligence augmentation. By seeking to field systems that do justice to the deep structure of everyday decision making, AAC hopes to enhance scientific and technological understanding. Applying folksonomy and machine learning techniques to the choice context will yield new practical insights that should be broadly useful elsewhere as well.
All About Choice also promises meaningful societal impacts. Systems that enable collaborative deliberation about important decisions strengthen both individual and collective effectiveness. Transparent systems promote accountability. By making such systems easily and inexpensively available, AAC hopes to raise the overall quality of decisions made.
A web-based system will enable distributed teams to review alternatives and fine-tune their decision-making processes. Individual users will draw upon extensive stores of objective and subjective information that leverage the collective learning of those who have been similarly situated.
Ultimate success will depend on the validity of several fundamental assumptions, namely (1) that a substantial number of people are sufficiently deliberative to use a well designed tool for a meaningful subset of their choices and (2) that weighted factor analysis, supplemented with qualitative and ordinal modes, and delivered via compelling graphical interfaces, is an effective foundation for such a tool.
Choosing well
The conceptual framework described above can be useful for thinking about decision processes in general. I’ve done preliminary work on a book with the above title. In the meantime, you might find this short article useful: Twelve Mantras for Technology Decisions. And this trio of posts that appeared in the Attorney at Work site:
· Helping Others to Choose Well
On a much more theoretical level there’s On Balance in the International Journal of Artificial Intelligence and Law, applying a choiceboxing approach to judicial reasoning. (Abstract only; sorry!)
Getting on my soap box
Choiceboxing largely remains a complex of ideas, not a fully realized implementation. Here are some of the principles behind its development:
· It emphasizes rich visualizations of choices in progress, beginning with three dimensional metaphors that seem to capture the fundamental dynamics of most situations, but with a commitment to ongoing interface improvement driven by actual participant experience.
· Such representations must deliver high transparency of rationale and support collaborative deliberation.
· It builds on social production of choice-support content, Wikipedia-like, yet goes well beyond textual forms of meaning communication, and is turbocharged by intelligent content refinement.
· It celebrates and empowers chooser autonomy through portability and relentless neutrality and privacy.
· It supports rich conversations among choosers and those with stakes in particular choices, rather than just being an apparatus to help one party make a decision.
· It builds on an deep and comprehensive model of choice making.
· It encompasses a full system of tools, content, communities, and social/economic players, rather than just being an “application.”
I have a naïve belief in the power of ideas, coupled, paradoxically, with deep cynicism about that power. But still believe that a universal resource that substantially improves both choice-making processes and results — while ensuring autonomy, neutrality, privacy, and transparency for participants — is within reach and highly worth achieving. Services that leverage interactive visualization and social production to enable collaborative deliberation should be part of the global ecosystem. As They May Choose was one attempt to capture that moon-shot vision.
Dear reader
“How can I help?” you might ask. Perhaps you could stage an intervention and convince me I’ve been going down a dead-end. Less dramatically, consider some of the ideas above, try out our current version, and share your honest feedback.
