Rapid prototype of an expert system

Expert systems can be useful for certain types of situations. However, this one confused fundamental objectives with actions in an unhealthy way - This is an egregious example of making inappropriate normative assumptions about the behavior of individuals.

Bigger models not needed

Skip Stiles worked many years as a congressional staffer, and by his own admission, wished for better climate change predictions. He seems to have seen the light, as highlighted in this guest post at Roger Pielke Jr.'s Blog. He is responding to the recent announcement of a $50 million multi-agency program to fund the development of better climate models. While I agree with Stiles that the $50 million could be better spent, I wish he'd reserve at least a bit of it for interdisciplinary research on how to structure science to be more useful to policy.

How's this for a rapid prototype model (Courtesy of xkcd.com):

Welcome to the cyborg planet!

Human Landscapes is an intriguing blog that I follow from time to time. This post on bringing nature in to create a cyborg planet is intriguing. Myself, I'm not sure more information about the planet is going to help, as long as we humans can't agree on what we want the planet to provide.

The AM process

A colleague, who may prefer to remain nameless, suggested that this comic be used as the primary diagram for the AM process:

Leverage points

Kelly, a commenter on an earlier post, pointed out Donella Meadows "Leverage Points: places to intervene in a system" paper. This is sort of a layperson's guide to "systems theory", but cast in a way that highlights the effectiveness of pushing on different points in a system. I find much to recommend this list:

12. Constants, parameters, numbers (such as subsidies, taxes, standards)

11. The size of buffers and other stabilizing stocks, relative to their flows

10. Structure of material stocks and flows (such as transport network, population age structures)

9. Length of delays, relative to the rate of system changes

8. Strength of negative feedback loops, relative to the effect they are trying to correct against

7. Gain around driving positive feedback loops

6. Structure of information flow (who does and does not have access to what kinds of information)

5. Rules of the system (such as incentives, punishment, constraints)

4. Power to add, change, evolve, or self-organize system structure

3. Goal of the system

2. Mindset or paradigm that the system — its goals, structure, rules, delays, parameters — arises out of

1. Power to transcend paradigms

The idea is that leverage points farther down the list (with smaller numbers) are more effective. Translating into my terms, I think leverage point 5 includes what I call "objectives" - the measurable things that people care about. People that have power to change the structure of the system - leverage point 4 - are either decision makers or meta-decision makers. Points 7 thru 11 are really different parts of what I do when modelling the consequences of decisions, expressed in a particular kind of modelling language. Leverage point 12 is what most people spend the most time arguing about - probably because it is the easiest thing to change.

So what's left? Leverage point 6 - information flows - is an interesting one because it forms the focus of discussions and failure to progress for the last 2 years on the Missouri River, IMHO. Who gets the information when, and then who acts on it (leverage point 4). All very unclear, and suggested clear pathways generate rapid, negative, strong (occasionally verging on violent!) reactions. The structured decision making framework places much emphasis on leverage point 5, but less on leverage points 4 and 6 - so maybe there is a lesson to be learned here.

Leverage point 3 - system goal - could be interpreted as fundamental objectives, but the writeup seems to imply that it is meant to be bigger than mere tactical or even strategic decisions, but bigger than that and you're really talking paradigms (leverage point 2). The most powerful leverage point is to achieve the power to transcend paradigms - which is in the territory of no thing / Buddhist enlightenment. Not that this is irrelevant to AM, but it is probably too difficult to realize frequently!

Interdisciplinary transformation

I've written before about transformative science, and I still don't really know what it looks like. However, I've been enlightened about Interdisciplinary research, or at least what NSF thinks interdisciplinary research is. At the UNL Research Fair Vikram Jaswal gave a talk on interdisciplinary opportunities at NSF, including a relatively clear and straightforward definition used by NSF - I can't find it online so I'll paraphrase: Research by a team or individual on a problem using concepts, techniques, data, and/or processes from two or more disciplines.

Vikram also pointed out an NSF report on the impacts of IGERT programs on the Academic institutions that receive them. On the very first page of the executive summary I found a keeper:

To carry out interdisciplinary research, one must have both disciplinary capability and interdisciplinary conversance.

I like this. The key is "disciplinary capability" - you have to be good at something to be an interdisciplinary scientist. I agree.

Policy and AM

I've been reading Kai Lee's classic "Compass and Gyroscope" from 1993 - squeezing in a bit of reading in between the frantic writing and meetings of "March Madness" (nothing to do with Basketball, in my case). In the section on whether or not learning can occur at the same time as conflict, Lee makes a concise statement of why AM practitioners must think about policy:

Theories of public policy are a sensible focus for this inquiry because if social learning is to occur in a durable way it must be reflected in policy.

Two key items - learning in AM is "social learning" - learning by the institutions and stakeholders. Second, it needs to be durable - to persist and affect decisions in the future. It is of little use if I learn something but that does not get passed on to the next manager, or better, incorporated into the institutional framework of which I am a part. Building opportunities for durable learning means making good public policies that allow for it.