Category: SystemDynamics

SD & ABM: Don't throw stones; build bridges

There’s an old joke:

Q: Why are the debates so bitter in academia?

A: Because the stakes are so low.

The stakes are actually very high when models intersect with policy, I think, but sometimes academic debates come across as needlessly petty. That joke came to mind when a colleague shared this presentation abstract:

Pathologies of System Dynamics Models or “Why I am Not a System Dynamicst”

by Dr. Robert Axtell

So-called system dynamics (SD) models are typically interpreted as a summary or aggregate representation of a dynamical system composed of a large number of interacting entities. The high dimensional microscopic system is abstracted-notionally if not mathematically-into a ‘compressed’ form, yielding the SD model. In order to be useful, the reduced form representation must have some fidelity to the original dynamical system that describes the phenomena under study. In this talk I demonstrate formally that even so-called perfectly aggregated SD models will in general display a host of pathologies that are a direct consequence of the aggregation process. Specifically, an SD model can exhibit spurious equilibria, false stability properties, modified sensitivity structure, corrupted bifurcation behavior, and anomalous statistical features, all with respect to the underlying microscopic system. Furthermore, perfect aggregation of a microscopic system into a SD representation will generally be either not possible or not unique.

Finally, imperfectly aggregated SD models-surely the norm-can possess still other troublesome features. From these purely mathematical results I conclude that there is a definite sense in which even the best SD models are at least potentially problematical, if not outright mischaracterizations of the systems they purport to describe. Such models may have little practical value in decision support environments, and their use in formulating policy may even be harmful if their inadequacies are insufficiently understood.

In a technical sense, I agree with everything Axtell says.

However, I could equally well give a talk titled, “pathologies of agent models.” The pathologies might include ungrounded representation of agents, overuse of discrete logic and discrete time, failure to nail down alternative hypotheses about agent behavior, and insufficient exploration of sensitivity and robustness. Notice that these are common problems in practice, rather than problems in principle, because in principle one would always prefer a disaggregate representation. The problem is that we don’t build models in principle; we build them in practice. In reality resources – including data, time, computing, statistical methods, and decision maker attention – are limited. If you want more disaggregation, you’ve got to have less of something else.

Clearly there are times when an aggregate approach could be misleading. To leap from the fact that one can demonstrate pathological special cases to the idea that aggregate models are dangerous strikes me as a gross overstatement. Is the danger of aggregating agents really any greater than the danger of omitting feedback by reducing scope in order to enable modeling disaggregate agents? Hopefully this talk will illuminate some of the ways that one might think about whether a situation is dangerous or not, and therefore make informed choices of method and tradeoffs between scope and detail.

Also, models seldom inform policy directly; their influence occurs through improvement of mental models. Agent models could have a lot to offer there, but I haven’t seen many instances where authors developed the lingo to communicate insights to decision makers at their level. (Examples appreciated – any links?) That relegates many agent models to the same role as other black-box models: propaganda.

It’s strange that Axtell is picking on SD. Why not tackle economics? Most economic models have the same aggregation issues, plus they assume equilibrium and rationality from the start, so any representational problems with SD are greatly amplified. Plus the economic models are far more numerous and influential on policy. It’s like Axtell is bullying the wimpy kid in the class, because he’s scared to take on the big one who smokes at recess and shaves in 5th grade.

The sad thing about this confrontational framing is that SD and agent based modeling are a match made in heaven. At some level disaggregate models still need aggregate representations of agents; modelers could learn a lot from SD about good representation of behavior and dynamics, not to mention good habits like units checking that are seldom followed. At the same time, SD modelers could learn a lot about emergent phenomena and the limitations of aggregate representations. A good example of a non-confrontational approach, recognizing shades of gray:

Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models

Hazhir Rahmandad, John Sterman

When is it better to use agent-based (AB) models, and when should differential equation (DE) models be used? Whereas DE models assume homogeneity and perfect mixing within compartments, AB models can capture heterogeneity across individuals and in the network of interactions among them. AB models relax aggregation assumptions, but entail computational and cognitive costs that may limit sensitivity analysis and model scope. Because resources are limited, the costs and benefits of such disaggregation should guide the choice of models for policy analysis. Using contagious disease as an example, we contrast the dynamics of a stochastic AB model with those of the analogous deterministic compartment DE model. We examine the impact of individual heterogeneity and different network topologies, including fully connected, random, Watts-Strogatz small world, scale-free, and lattice networks. Obviously, deterministic models yield a single trajectory for each parameter set, while stochastic models yield a distribution of outcomes. More interestingly, the DE and mean AB dynamics differ for several metrics relevant to public health, including diffusion speed, peak load on health services infrastructure, and total disease burden. The response of the models to policies can also differ even when their base case behavior is similar. In some conditions, however, these differences in means are small compared to variability caused by stochastic events, parameter uncertainty, and model boundary. We discuss implications for the choice among model types, focusing on policy design. The results apply beyond epidemiology: from innovation adoption to financial panics, many important social phenomena involve analogous processes of diffusion and social contagion. (Paywall; full text of a working version here)

Details, in case anyone reading can attend – report back here!

Thursday, October 21 at 6:00 – 8:00 PM ** New Time **

Networking 6:00 – 6:45 PM (light refreshments) Presentation 6:45 – 8:00 PM Free and open to the public

** NEW Location **

Booz Allen Hamilton – Ballston-Virginia Square

3811 N. Fairfax Drive, Suite 600

Arlington, VA 22203

(703) 816-5200

Between Virginia Square and Ballston Metro stations, between Pollard St.

and Nelson St.

On-street parking is available, especially on 10th Street near the Arlington Library.

There will be a Booz Allen representative at the front of the building until 7:00 to greet and escort guests, or call 703-627-5268 to be let in.

RSVP by e-mail to Nicholas Nahas, nahas_nicholas@bah.com<mailto:nahas_nicholas@bah.com>, in order to have a rough count of attendees prior to the meeting. Come anyway even if you do not RSVP.

By METRO:

Take the Orange Line to the Ballston station. Exit Metro Station, walk towards the IHOP (right on N. Fairfax) continue for approximately 2-3 blocks. Booz Allen Hamilton (3811 N. Fairfax Dr. Suite 600) is on the left between Pollard St. and Nelson St.

OR Take the Orange Line to the Virginia Square station. Exit Metro Station and go left and walk approximately 2-3 blocks. Booz Allen Hamilton (3811 N. Fairfax Dr. Suite 600) is on the right between Pollard St. and Nelson St.

Brian Eno, meet Stafford Beer

Brian Eno reflects on feedback and self-organization in musical composition, influenced by the organization of complex systems in Stafford Beer’s The Brain of the Firm.

Stafford Beer was a member of the cybernetics thread of systems thought (if that sounds baffling, read George Richardson’s excellent book on the evolution of thinking about systems).

Interactive diagrams – obesity dynamics

Food-nutrition-health-exercise-energy interactions are an amazing nest of positive feedbacks, with many win-win opportunities, but more on that another time.

Instead, I’m hoisting an interesting influence diagram about obesity from the comments. At first glance, it’s just another plate of spaghetti.

ForesightObesity

But when you follow the link (do it now), there’s an interesting innovation: the diagram is interactive. You can zoom, scroll, and highlight particular sectors and dynamics. There’s some narrative here and here. (Update: the interactive link seems to be down, but the diagram is still here: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/295153/07-1177-obesity-system-atlas.pdf)

It took me a while to decide whether I’d call this a causal loop diagram or not. I think the primary distinction between a CLD and other kinds of mindmaps or process diagrams is the use of variables. On a CLD, each label represents a quantity that can vary, with a definite direction – TV Watching, Stress, Use of Medicines. Items on other kinds of diagrams might represent events or fuzzier constellations of concepts. This diagram doesn’t have link polarities (too bad) or loop polarities (which would be pretty incomprehensible anyway), but many other CLDs also avoid such labels for simplicity.

I think there’s a lot of potential for further exploration of this idea. There’s a lot you could do to relate structure to behavior, or at least to explain the rationale for structure (both shortcomings of the diagram). Each link, for example, could have its tale revealed when clicked, and key loops could be animated individually, with stories told. Drill-down could be extended to provide links between top-level subsystem relationships and more microscopic views.

I think huge diagrams like the one above are always going to be overwhelming to a layperson. Also, it’s hard to make even a small CLD good, so making a big one really accurate is tough. Therefore, I’d rather see advanced CLD presentations used to improve the communication of simpler stories, with a few loops. However, big or small, there might be many common technological benefits from dedicated diagramming software.

Gallatin County's Zoning Enforcement Trap

I’m playing a big role in a local effort to get the regulations of our zoning district enforced in the case of an egregious violation. Our planning and zoning commission’s habit, and apparent preference in this case, is not to enforce. Instead, it is proposed to enable the violation through a PUD amendment, and issue a trivial fine ($200, or 0.2% of the stated value of the structure).

Unfortunately, this proposal is illegal, because it contradicts existing covenants and a variety of goals and specific provisions of our General Plan and Zoning Regulation. This action might make sense if it were a naked political ploy to undermine the zoning through administrative rather than legislative means, which I hope is not the case. I think it is more likely an effort to “play nice” with violators and to avoid costly enforcement action.

If so, the resulting weak enforcement posture is a short-sighted avoidance of conflict, that encourages far more problems in the long run. As the diagram below illustrates, backing down on the case at hand solves the immediate problem, but has terrible consequences.

Enforcement Dynamics

  • The precedent for non-enforcement and amendments to legalize violations erodes the legal basis for future enforcement actions.
  • Accommodation creates an expectation of forgiveness, encouraging owners and builders to violate in the future.
  • Exceptions created to accommodate violations make planning documents and title histories more complex, creating more opportunities for errors.

These side effects of lax enforcement accumulate. As violations mount, time that could be spent on productive activity (ensuring a thorough permitting process, or revising zoning regulations to clarify standards and streamline processes) gets squeezed out by time wasted on enforcement.

These reinforcing feedbacks create a deadly trap, into which the unsuspecting can easily step. Once triggered, the vicious cycle creates more pressure to relax enforcement standards, capturing the county in an undesirable equilibrium with many violations and no meaningful enforcement. Ultimately, the citizens (who initiated the zoning district) suffer from the side effects of density granted to violators, that is unavailable to those who comply with the law.

Fortunately, with a little fortitude, the process can be reversed. A single forceful enforcement action has a salutary effect on expectations, stemming the tide of violations and freeing up time for the improvement of regulations. There’s still the hangover of side effects of past accommodation to contend with, but surely the withdrawal is better than the addiction to accommodation.

Cap & trade is dead. Long live cap & trade?

Democrats have pulled the plug on a sweeping energy bill this year. There is no heir apparent. This is not cause for panic. In climate, as in education, there are no emergencies.

However, the underlying reasons may be cause for panic. It seems that voters are unwilling to accept any policy that will significantly raise the price of emissions. Given that price is a predominant information carrier in our economy, other polices are unlikely to work efficiently, absent a price signal. That leaves us in a bit of a pickle. What to do?

If you don’t want to buck public opinion, advise the people to invest in (then pray for) a technological miracle. Ask yourself, “Do I feel lucky?” It might even work.

Alternatively, you might conclude that the public hasn’t quite grasped the nature of the problem – that wait and see is not a good policy in systems with long delays. But then you’d be accused of scientism, for the equivalent of challenging the efficient market hypothesis or the notion that the customer is always right. That’s rather puzzling, given that there’s direct evidence that people don’t intuitively appreciate the dynamics of accumulation, and that snowstorms in the East cause half of Americans to question the reality of climate change.

The anti-scientism, pro-technology crowd takes opposition to meaningful mitigation policy as a sure sign that the public is on to something. The wisdom of crowds is powerful when there’s diverse information and rapid feedback, as in price discovery through a market. But it has a pretty disastrous history in the runup to bubbles and other catastrophes, as we’ve recently seen. Surely there are some legitimate worries about current climate proposals (I’ve expressed a number here), but it doesn’t follow that pricing emissions is a bad decision.

So, what’s a modeler to do? Opening up political debates is a good idea, though not quite in the way that I think proponents intend. We already have plenty of political debates. The problem is that they tend to lack ready access to scientific or other information that can be agreed upon or at least presented in a way that permits testing of hypotheses against data or evaluation of decisions against contingencies. That means that questions of values and distribution of benefits (which politics is rightfully about) get mixed up with muddled thinking about science, economics, and social system dynamics.

The solution typically proposed is to open up science and models to more public scrutiny. That’s a good idea for a variety of reasons, but by itself it’s a losing proposition for scientists- they get all the criticism, and the public process doesn’t assimilate much of their insights. What’s needed is a fair exchange, where everyone shows their hands. Scientists make their stuff accessible, and in return participants in policy debates actually use it, and additionally submit to formalization of their arguments to facilitate shared understanding and testing.

Coming back to cap & trade, I don’t see that the major political players are willing to do that. Following a successful round of multi-stakeholder workshops that brought a systems perspective to conversations about climate policy, funded by the petro industry in California, we spent a fair amount of time marketing the idea of a model-assisted deliberation process targeted at shared design of federal climate policy. Lobbyists at some of the big stakeholders told us very forthrightly that they were unwilling to engage in any process with an outcome that they couldn’t predict and control.

In an environment where everyone’s happy with their own entrenched position, their isn’t much hope for a good solution to emerge. The only solution I see is to make an end run around the big players, and go straight to the public with better information, in order to expand the set of things they’ll accept. I hope there’s time for that to work.

Policy Resistance – Immigration & Prohibition

Complex systems find many ways of resisting or evading pressures, resulting in policy failure, backlashes, whack-a-mole games and other unintended consequences. Some great examples just wandered by my desk:

Via Economist’s View:

Immigration reform has a long history of unintended consequences: More than two decades of increased enforcement since the passage of the Immigration Reform and Control Act of 1986 has done little to reduce the number of illegal immigrants. In fact, it seems to have increased their numbers. …

Princeton University sociologist Douglas Massey pointed out … that measures to secure the border seemed to produce almost the opposite of what was intended. … With increasing border enforcement, workers who used to shuttle between jobs in California or Texas and home in Zacatecas or Michoacán simply began to stay put and sent for their families, becoming permanent, if sometimes reluctant, residents. According to Massey, post-IRCA border enforcement may have increased the size of the permanent Mexican population in the United States by a factor of nearly four.

From a great article on Wayne Wheeler, The Man Who Turned Off the Taps, in Smithsonian:

But for all his political might, Wheeler could not do what he and all the other Prohibitionists had set out to do: they could not purge alcoholic beverages from American life. Drinking did decline at first, but a combination of legal loopholes, personal tastes and political expediency conspired against a dry regime.

As declarative as the 18th Amendment was—forbidding “the manufacture, sale, or transportation of intoxicating liquors”—the Volstead Act allowed exceptions. You were allowed to keep (and drink) liquor you had in your possession as of January 16, 1920; this enabled the Yale Club in New York, for instance, to stockpile a supply large enough to last the full 14 years that Prohibition was in force. Farmers and others were allowed to “preserve” their fruit through fermentation, which placed hard cider in cupboards across the countryside and homemade wine in urban basements. “Medicinal liquor” was still allowed, enriching physicians (who generally charged by the prescription) and pharmacists (who sold such “medicinal” brands as Old Grand-Dad and Johnnie Walker). A religious exception created a boom in sacramental wines, leading one California vintner to sell communion wine—legally—in 14 different varieties, including port, sherry, tokay and cabernet sauvignon.

By the mid-’20s, those with a taste for alcohol had no trouble finding it, especially in the cities of the East and West coasts and along the Canadian border. At one point the New York police commissioner estimated there were 32,000 illegal establishments selling liquor in his city. In Detroit, a newsman said, “It was absolutely impossible to get a drink…unless you walked at least ten feet and told the busy bartender what you wanted in a voice loud enough for him to hear you above the uproar.” Washington’s best-known bootlegger, George L. Cassiday (known to most people as “the man in the green hat”), insisted that “a majority of both houses” of Congress bought from him, and few thought he was bragging.

Worst of all, the nation’s vast thirst gave rise to a new phenomenon—organized crime, in the form of transnational syndicates that controlled everything from manufacture to pricing to distribution. A corrupt and underfunded Prohibition Bureau couldn’t begin to stop the spread of the syndicates, which considered the politicians who kept Prohibition in place their greatest allies. Not only did Prohibition create their market, it enhanced their profit margins: from all the billions of gallons of liquor that changed hands illegally during Prohibition, the bootleggers did not pay, nor did the government collect, a single penny of tax.

The prohibition article also poses an interesting puzzle. If prohibition was more or less quickly and broadly unpopular, how did it get passed by such landslide margins in the first place? I can’t believe that ignorance of the possible outcome was universal, so there must have been some powerful positive feedback behind the initial passage of the policy. Perhaps it was a tipping point effect: once a vote becomes sufficiently lopsided, fewer and fewer politicians want to be on the losing side of a landslide vote, so they join the herd. A modern analogy might be the post-9/11 authorization of the Iraq war.

Get a lawyer

That’s really the only advice I can give on models and copyrights.

Nevertheless, here are some examples of contract language that may be illuminating. Bear in mind that I AM NOT A LAWYER AND THIS IS NOT LEGAL ADVICE. I provide no warranty of any kind and assume no liability for your use or misuse of these examples. There are lots of deadly details, regional differences, and variations in opinion about good contract terms. Also, these terms have been slightly adapted to conceal their origins, which may have unintended consequences. Get an IP lawyer to review your plans before proceeding.

Continue reading “Get a lawyer”

Models and copyrights

Or, Friends don’t let friends work for hire.

opencontent

Image Copyright 2004 Lawrence Liang, Piet Zwart Institute, licensed under a Creative Commons License

Photographers and other media workers hate work for hire, because it’s often a bad economic tradeoff, giving up future income potential for work that’s underpaid in the first place. But at least when you give up rights to a photo, that’s the end of it. You can take future photos without worrying about past ones.

For models and software, that’s not the case, and therefore work for hire makes modelers a danger to themselves and to future clients. The problem is that models draw on a constrained space of possible formulations of a concept, and tend to incorporate a lot of prior art. Most of the author’s prior art is probably, in turn, things learned from other modelers. But when a modeler reuses a bit of structure – say, a particular representation of a supply chain or a consumer choice decision – under a work for hire agreement, title to those equations becomes clouded, because the work-for-hire client owns the new work, and it’s hard to distinguish new from old.

The next time you reuse components that have been used for work-for-hire, the previous client can sue for infringement, threatening both you and future clients. It doesn’t matter if the claim is legitimate; the lawsuit could be debilitating, even if you could ultimately win. Clients are often much bigger, with deeper legal pockets, than freelance modelers. You also can’t rely on a friendly working relationship, because bad things can happen in spite of good intentions: a hostile party might acquire copyright through a bankruptcy, for example.

The only viable approach, in the long run, is to retain copyright to your own stuff, and grant clients all the license they need to use, reproduce, produce derivatives, or whatever. You can relicense a snippet of code as often as you want, so no client is ever threatened by another client’s rights or your past agreements.

Things are a little tougher when you want to collaborate with multiple parties. One apparent option, joint ownership of copyright to the model, is conceptually nice but actually not such a hot idea. First, there’s legal doctrine to the effect that individual owners have a responsibility not to devalue joint property, which is a problem if one owner subsequently wants to license or give away the model. Second, in some countries, joint owners have special responsibilities, so it’s hard to write a joint ownership contract that works worldwide.

Again, a viable approach is cross-licensing, where creators retain ownership of their own contributions, and license contributions to their partners. That’s essentially the approach we’ve taken within the C-ROADS team.

One thing to avoid at all costs is agreements that require equation-level tracking of ownership. It’s fairly easy to identify individual contributions to software code, because people tend to work in containers, contributing classes, functions or libraries that are naturally modular. Models, by contrast, tend to be fairly flat and tightly interconnected, so contributions can be widely scattered and difficult to attribute.

Part of the reason this is such a big problem is that we now have too much copyright protection, and it lasts way too long. That makes it hard for copyright agreements to recognize where we see far because we stand on the shoulders of giants, and distorts the balance of incentives intended by the framers of the constitution.

In the academic world, model copyright issues have historically been ignored for the most part. That’s good, because copyright is a hindrance to progress (as long as there are other incentives to create knowledge). That’s also bad, because it means that there are a lot of models out there that have not been placed in the public domain, but which are treated as if they were. If people start asserting their copyrights to those, things could get messy in the future.

A solution to all of this could be open source or free software. Copyleft licenses like the GPL and permissive licenses like Apache facilitate collaboration and reuse of models. That would enable the field to move faster as a whole through open extension of prior work. C-ROADS and C-LEARN and component models are going out under an open license, and I hope to do more such experiments in the future.

Update: I’ve posted some examples.