What Exactly is a Tradespace?
A brief introduction to MATE and EEA
Since this publication is called The Tradespace, we should probably clarify what exactly we mean by that word. Tradespace exploration — sometimes referred to by similar names such as trade space analysis or tradeoff analysis — is a broad category of decision analysis techniques that generally focus on the comparison of alternatives (potential solutions to the decision) in terms of the way they “trade” different qualities.
For example, if you were in the market for a new phone, you might compare them using qualities like screen size, battery life, etc. When we refer to “the tradespace” directly, we typically mean this concept — many different alternatives, many different ways to compare them. And since, all else equal, phones with larger screens have correspondingly lower battery life, a fundamental tension exists between these two specs: we can’t have the best of both worlds and will have to balance our needs by finding a desirable tradeoff between them.
Multi-Attribute Tradespace Exploration (MATE) is a methodology originally developed at MIT in 2000, then refined and expanded over the years by the work of scores of researchers and grad students. To oversimplify slightly, you can think of MATE as a set of guidelines for doing tradespace exploration to increase your chance of finding the best alternative among the many possibilities. At its core, MATE is a value-focused thinking approach, beginning with (and always returning to) the qualities/criteria/parameters/attributes that define a good solution.
Ever spend hours collecting data for a specific metric only to realize later that it didn’t affect the decision? Not anymore! Applying MATE begins with understanding what it is that stakeholders care about: once we know that, we can collect or generate data for many different potential alternatives and analyze/explore that data to understand what is achievable.
The most common visualization of “the tradespace” is a scatterplot, where each point represents one potential solution (alternative) and the axes show benefit and cost metrics. This simple visualization of the tradeoffs between these metrics helps analysts quickly identify desirable alternatives. For almost every decision, it takes more cost to get more benefit: a fundamental tension more universal than the earlier screen size vs. battery life example. Alternatives with the most benefit for a given amount of cost are said to be “in the Pareto set” or “on the Pareto front” and are generally the most desirable.
So then what is Epoch-Era Analysis (EEA)? EEA was developed alongside MATE starting in 2006, and empowers any aspiring MATE user to add a key dimension to their problem: uncertainty. If applying MATE helps you generate a tradespace of potential solutions and explore their value, applying EEA helps you explore the ways that value might change due to factors outside of your control.
What would my tradespace look like if one of my assumptions was different? Which variables have the biggest impact on my value? Is it possible to find a solution that is robust to all this uncertainty? These are all questions that, left unanswered, leave people feeling unconfident in their decision. With EEA, it is possible to directly compare, contrast, and evaluate hundreds of scenarios either one-at-a-time or as a group.
Our software EpochShift takes its name from a phenomenon described by EEA: a change in the state of the world, with no sign of it changing back. We think EpochShift represents exactly that: a change in how people can think about, visualize, and analyze their decisions — and after you try it you won’t want to go back. We’ll have future posts in this space providing more details on both MATE and EEA, as well as advice for beginners on how to formulate their problems to maximize the insights they can gain. Stay tuned!
