The above image is entirely accurate: whenever I'm shopping or taking out the trash, I'm thinking about new scenarios for dealing with everyday material life. I'm looking into the construction and use of scenario-based evaluation systems for material problems. This concerns the application of techniques from computer science and machine learning to problems in the engineering and governance of systems with multi-generational impacts, focusing on closed-loop supply chains and other infrastructural concerns with recurring, sequential processes, notably including manufacturing, transportation, energy, agriculture, and retail.
Currrent Problems and Questions
The Feynman Problem-Solving Algorithm:- write down the problem
- think very hard
- write down the answer
In the spririt of this friendly academic teasing, I've decided that a useful purpose for this site is to write down questions I'm currently thinking about, and what I've thought about them so far. Although they will get their own pages, I'm putting a couple of starting questions up. If you have any insights on these topics, please feel free to email me (john at this domain).
The multiple purposes of scenarios: I'm mainly interested in scenarios for the range of things they can accomplish. An elaboration for how I suspect this might work can be found here.
Eliciting causal impacts: Right now I'm working on questions of how does even one appropriately elicit scenarios for governing risky situations with multiple stakeholders, when the participants potentially have all kinds of differences in their worldview. (This has stemmed from my research methods projects, and I'm very exited about this, and am writing a paper and software tools now). A related question is how does one represent and visualize the conflicting accounts emerging from the testimony of multiple stakeholders?
Rendering causal impacts, emerging changing behavior: How do you aggregate nearly insignificant small-scale individual choices into long-term, large-scale, high-impact behaviors in a way that's useful for making situational decisions? For example, how to do map a particular purchase as an exemplar of larger-scale trajectories of materials, energy, and waste? As another example, how do you map a given meal choice on to the nutrition and fitness of a region? And how do you render that information in a manner that makes it immediately useful?
Emerging cradle-to-cradle: One interesting special case of this is the emerging cradle-to-cradle problem. What's the minimum amount of information one needs to capture to make emergent cradle-to-cradle behavior feasible? Cradle-to-cradle issues interest me quite a bit, because of the temporal effect on values: no matter the social values a given society might determine is appropriate, using up a resource thermodynamically will affect future societies with different social arrangement. This leads to some frighteningly easy decision-theoretic planning perspective: non-discounted marginal loss spikes to infinity. My first, non-technical (ok, what qualifies for me as non-technical) stab at this can be found here.
Making causal information "portable": Even beyond representing causal information in the first place, I think there are some challenges in making it "portable". Everyone knows there are active volcanos in Iceland, yet nobody can be practically expected to use that information to assist their business travel planning. How to do you pull in what you already "know"? Also, how does one assemble this common knowledge? Can it be mined from text?
Design from the perspective of statistical processes: How do you know when your understanding of potential causal forces is complete enough? I think that design and ethnography teach us to look further than we otherwise might (to bias exploration), and there could be interesting discoveries in how design works from a statistical perspective (similar to recent work in cognitive developmental psychology and the psychology of science). Further, I suspect this kind of analysis may have some interesting things to say about the validation of foresight work. In particular, I hope that there will be some thing to the approach of looking at priors over structures (in the style of Josh Tenenbaum's research).
Making design and diagrams "portable": I think of design as being very project-driven: you try a bunch of things, you finish one of them to completion, and then you take whatever you learn, but the models that you often build end up in the trash. In machine learning, there's a problem called "transfer learning", where you ask given that you know one kind of statistics, what can you infer about a different relationship. There's possibly a design analog, where after you've illustrated one set of relationships, how do you (or somebody else) recover those connections for a different project?
Materialist foundations for ontology What would a computational implementation of a materialist ontology (i.e. Manuel DeLanda's work) look like?
How does institutional formation work? How does society organize to form responses to repeated problems?
Data sets
Here are some data sets I've long thought I might enjoy. The ideal statistics for each would, where applicable, span worldwide or a otherwise have a strong cross-cultural sampling, be separated by region, and spanning as far back as 2000 years ago; and include totals, or where not available averages, medians, and quartiles (I'll settle for variances)- occupational statistics
- inventories of durable goods owned in a lifetime, ideally separated into each decade of life
- inventories of consumable goods used, monthly or weekly
- major flows of fuel and mined materials, as well as known reserves; including aggregates per region for renewables and other distributed streams
- waste statistics
- dates of inventions and installations of new kinds of equipment (i.e. the first lathe in North America)
- institution statistics (i.e. number and population of schools, prisons, hospitals, financial institutions, and types of businesses)
- transportation
- agricultural production (crops planted, volume, and in particular nutritional density)
- linguistic and word usage statistics
