Tag Archives: simulation

macroeconomic models and tax cuts

The Economist has a piece on macroeconomic models used to evaluate tax policy.

Lurk near PhD economists online or at conferences, and you will hear them talk about “crisis in macro”. They mean that the models and assumptions most dominant among macroeconomists have failed repeatedly since 2007 to predict or even describe what’s happening. A defence, popular among academics, goes like this: we did get it wrong, but as responsible social scientists, we’re busy and fascinated right now, trying to figure out what was broken and how to fix it. It is a fair defence. In particular young macroeconomists have been using bigger datasets and faster computers to more accurately predict human behaviour. Economists are more likely to accept now, for example, that people with and without access to credit or wealth react differently to the same policy, an idea that is slowly working its way into models at central banks and even at the Joint Committee on Taxation.

This progress is unfortunate for Republicans. In the 1990s social science was on their side. Because data and computing power were harder to come by, macroeconomic models relied on thought experiments. The seminal model showing the ideal capital-gains tax rate as zero, for example, dates to 1986. It assumes that the economy consists of only one person. Also, she is immortal. The Wonder Woman economy, if you will. That model is now interesting only for a lecture on the history of economic thought. We’ve moved on, macroeconomists protest. But economists have. And Republicans haven’t…

But if you are going to insist on modeling the future and then planning around it, you have to do it right. The economists at the Joint Committee on Taxation are thoughtful. They read the most recent research. They examine their own models and, when they can, update them—conservatively. If, as Republicans have been insisting for 20 years, we have to assess our tax policies with dynamic scoring, there is no better way to do it than through the JCT. Unfortunately, as modeling has improved, it has not improved in the direction Republicans prefer, which leaves them where they are now. They wanted social science in policy-making, and they got it, in the form of a $1trn tax bill.

I don’t know how any ethical person can support the Republican party right now. They don’t care about facts, logic, or evidence, and certainly not economic growth or raising the living standards of their constituents. They are blantantly and shamelessly committed to lining the pockets of their big-business funders. It’s corrupt, undemocratic and shameful.

synergy, uniqueness, and redundancy in interacting environmental variables

This is a bit over my head, but one thing I am interested in is analyzing and making sense of a large number of simultaneous time series, whether measured in the environment, the economy, or output of a computer model. This can easily be overwhelming, so one place people often start is trying to figure out which time series are telling essentially the same story, or directly opposite stories. Understanding this allows you to reduce the number of variables you need to analyze to a more manageable number. Time series make this more complicated though, because two variables could be telling the same or opposite stories, but if the signals are offset in time, simple ways of looking at correlation may not lead to the right conclusions. With simulations you have yet another set of complicating factors, which is the implicit links between your variables, intended or not, and whether they exist in the real world or not.

Temporal information partitioning: Characterizing synergy, uniqueness, and redundancy in interacting environmental variables

Information theoretic measures can be used to identify non-linear interactions between source and target variables through reductions in uncertainty. In information partitioning, multivariate mutual information is decomposed into synergistic, unique, and redundant components. Synergy is information shared only when sources influence a target together, uniqueness is information only provided by one source, and redundancy is overlapping shared information from multiple sources. While this partitioning has been applied to provide insights into complex dependencies, several proposed partitioning methods overestimate redundant information and omit a component of unique information because they do not account for source dependencies. Additionally, information partitioning has only been applied to time-series data in a limited context, using basic pdf estimation techniques or a Gaussian assumption. We develop a Rescaled Redundancy measure (Rs) to solve the source dependency issue, and present Gaussian, autoregressive, and chaotic test cases to demonstrate its advantages over existing techniques in the presence of noise, various source correlations, and different types of interactions. This study constitutes the first rigorous application of information partitioning to environmental time-series data, and addresses how noise, pdf estimation technique, or source dependencies can influence detected measures. We illustrate how our techniques can unravel the complex nature of forcing and feedback within an ecohydrologic system with an application to 1-minute environmental signals of air temperature, relative humidity, and windspeed. The methods presented here are applicable to the study of a broad range of complex systems composed of interacting variables.

UPS trucks don’t turn left

UPS claims to save a lot of time, fuel, and reduce accidents significantly by avoiding left turns at intersections with no left turn signals. In other words, they circle right until they get where they need to go, and it ends up saving time, energy, and lives. I’m glad to see this – as someone who makes 99% of my own trips on foot, I know vehicles turning left with fast-moving oncoming traffic are incredibly risky for pedestrians. Some people are jerks and have no respect for human life. But during those other 1% of trips where I am the driver, I understand why even well-intentioned, ethical people can put pedestrians at risk – because you are so focused on the cars and making a safe turn you are just not looking for pedestrians. I think most left turns should be eliminated (or left turn signals put in, or pedestrian scrambles, or lights turned off in favor of stop signs) purely on safety grounds, but if doing that wouldn’t even cost drivers any time or money the argument gets even stronger.

modeling the Maya collapse

This interesting study included a computer model of how drought and agricultural practices could have combined to destroy the ancient Mayan civilization.

Conceptualizing sociohydrological drought processes: The case of the Maya collapse

With population growth, increasing water demands and climate change the need to understand the current and future pathways to water security is becoming more pressing. To contribute to addressing this challenge, we examine the link between water stress and society through socio-hydrological modeling. We conceptualize the interactions between an agricultural society with its environment in a stylized way. We apply the model to the case of the ancient Maya, a population that experienced a peak during the Classic Period (AD 600-830) and then declined during the ninth century. The hypothesis that modest drought periods played a major role in the society’s collapse is explored. Simulating plausible feedbacks between water and society we show that a modest reduction in rainfall may lead to an 80% population collapse.Population density and crop sensitivity to droughts, however, may play an equally important role. The simulations indicate that construction of reservoirs results in less frequent drought impacts, but if the reservoirs run dry, drought impact may be more severe and the population drop may be larger.

skyscraper game

This looks pretty cool – an iPhone game for kids that lets them look inside a skyscraper.

In a few light swipes and taps, users “create” a made-up skyscraper by adding floors and choosing the color of the facade. On the app’s sidebar, select a tiny I-beam button to play a game where adding boulders, elephants, and sailboats sinks your building deep and lopsided into its foundation. An elevator icon takes you to an interactive view of interior life—families in their kitchens, watching television, tiptoe-ing through bedrooms. The details are incredibly ornate, especially in another mode, accessed by clicking on a little water drop, where you clog toilets and set fires on different floors. Watch how the building (which gets an anthropomorphic touch) reacts. They say if walls could talk…

Problems just keep backing up.
(Screenshot of “Skyscrapers” by Tinybop)

With virtually no text, the app invites you to play by intuiting through touch and iconography. Youngsters, presumably raised on the logic of iPhones, are the audience targeted by the app’s developer, Tinybop. “Skyscrapers” is the seventh in Tinybop’s “Explorer’s Library,” series, which “introduces kids to STEAM topics they learn about in school,” according to a spokesperson.

I looked at the Explorer’s Library and they have a number of cool simulation apps for kids, like plants, the human body, and weather. I think I might start with one of those rather than a skyscraper. I am always on the lookout for a really good ecosystem simulation for kids.

weather forecasting history

I recently wrote about earthquake forecasting and how many scientists think it is essentially impossible. But it is interesting to compare that with the state of weather forecasting in the 1800s:

Before the Royal Charter storm, FitzRoy had been agitating in London for government funding for collection of weather data. He and other Victorian men of meteorology knew that the more they could parse what the weather had done in the past, the better they could warn what it might do in the future. FitzRoy called the concept “forecasting.” To show just how ludicrous that idea seemed at the time, Moore unearths a telling 1854 Commons debate. When a scientifically enthusiastic member of Parliament suggested that amassing weather observations from sea and land could someday mean “we might know in this metropolis the condition of the weather 24 hours beforehand,” laughter broke out raucously enough to stop the proceeding.

agent-based social system modeling

One approach to agent-based social system modeling is the Institutional Analysis and Development Framework developed by Elinor and Vincent Ostrom at the Indiana University:

The IAD Framework offers researchers a way to understand the policy process by outlining a systematic approach for analyzing institutions that govern action and outcomes within collective action arrangements (Ostrom, 2007, 44). Institutions are defined within the IAD Framework as a set of prescriptions and constraints that humans use to organize all forms of repetitive and structured interactions (Ostrom, 2005, 3).  These prescriptions can include rules, norms, and shared strategies (Crawford and Ostrom 1995; Ostrom 1997). Institutions are further delineated as being formal or informal; the former characterized as rules-in-form and the latter as rules-in-use.

The IAD framework identifies key variables that researchers should use in evaluating the role of institutions in shaping social interactions and decision-making processes.  The analytical focus of the IAD is on an “action arena”, where social choices and decisions take place. Three broad categories of variables are identified as influencing the action arena:  institutions or rules that govern the action arena, the characteristics of the community or collective unit of interest, and the attributes of the physical environment within which the community acts (Ostrom 1999; Ostrom 2005). Each of these three categories has been further delineated by IAD scholars into relevant variables and conditions that can influence choices in the action arena.  For instance, the types of rules that are important in the IAD include entry and exit rules, position rules, scope rules, payoff rules, aggregation rules, authority rules, and information rules.  Key characteristics of the community can include factors such as the homogeneity of its members or shared values.  Biophysical variables might include factors such as the mobility and flow of resources within an action arena.

The IAD further defines the key features of “action situations” and “actors” that make up the action arena. The action situation has seven key components: 1) the participants in the situation, 2) the participants’ positions, 3) the outcomes of participants’ decisions, 4) the payoffs or costs and benefits associated with outcomes, 5) the linkages between actions and outcomes, 6) the participants’ control in the situation, and 7) information. The variables that are essential to evaluating actors in the action arena are 1) their information processing capabilities, 2) their preferences or values for different actions, 3) their resources, and 4) the processes they use for choosing actions.

Here are a couple papers that describe attempts to operationalize this framework:

MAIA: a Framework for Developing Agent-Based Social Simulations

Modelling socio-ecological systems with MAIA: A biogas infrastructure simulation

agent based modeling

Agent-based modeling is one of those things I want to play around with some day if I theoretically ever had some time.

Modelling domestic water demand: An agent based approach

The urban water system is a complex adaptive system consisting of technical, environmental and social components which interact with each other through time. As such, its investigation requires tools able to model the complete socio-technical system, complementing “infrastructure-centred” approaches. This paper presents a methodology for integrating two modelling tools, a social simulation model and an urban water management tool. An agent based model, the Urban Water Agents’ Behaviour, is developed to simulate the domestic water users’ behaviour in response to water demand management measures and is then coupled to the Urban Water Optioneering Tool to calculate the evolution of domestic water demand by simulating the use of water appliances. The proposed methodology is tested using, as a case study, a major period of drought in Athens, Greece. Results suggest that the coupling of the two models provides new functionality for water demand management scenarios assessment by water regulators and companies.