Tag Archives: planning

MEMOP, MOP, and SMOP

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These are some more decision-making frameworks I hadn’t heard of, at least by name. I am hopeful that AI can help techniques like this make the transition from gown to town.

Developing Robust Management Pathways for Nutrient Pollution in Watersheds Under Climate Uncertainty

Nutrient management represents an enduring effort toward sustainability. However, long-term management planning faces notable challenges, mainly due to substantial investments required under uncertainty of forthcoming climate. To address these challenges, this paper proposes and tests a Multi-climate-scenario (MCS) Multi-epoch Multi-objective Planning (MEMOP) framework (in combination MCS-MEMOP). This framework divides the long-term planning horizon into multiple epochs, allowing nutrient mitigation measures (e.g., fertilization management, filter strip) to be initiated at any epoch, each with its own water quality and investment constraints. To tackle climate uncertainty, it incorporates principles of Robust Decision-Making. MCS-MEMOP generates solution pathways outlining the timeline and progression of management measures, tested here for a case of a small, agriculture-dominated watershed. Considering a single climate scenario, the MEMOP method was compared with Multi-objective Planning (MOP) and Stepwise MOP (SMOP) for a 25-year nutrient management horizon, using the SWAT model to evaluate the test case water quality effects of solution pathways. Results show that MEMOP’s multi-epoch approach generates a larger and more diverse set of solutions than MOP and SMOP, offering greater flexibility to select optimal trade-offs among objectives. Additionally, MEMOP solutions exhibit superior cost-effectiveness compared to MOP and SMOP solutions. Applied separately to different climate scenarios, the MEMOP results show that changed climate conditions may significantly alter the Pareto front. In contrast, MCS-MEMOP yields robust solutions that can consistently satisfy 72%∼89% of epoch-specific constraints under new climate conditions in the test case, with a cost increase of 12% that reflects the price of addressing climate uncertainty in this case.

The Tyranny of Small Decisions

I have thought about this concept on my own, but didn’t know about the work of Alfred Kahn. Basically, the idea is that many small daily decisions made at inappropriately low levels within an organization can lead to drift from the organizations mission or goals, and ultimately undesirable outcomes or even failure. All these individual decisions can seem rational and well-intentioned to the people making them.

This all makes sense to me, and I have seen it play out in organizations. I do think it is a strong argument for planning. Organizations that don’t have appropriate decision making processes in place are going to fail. For even for organizations that do, the people making the decisions need to understand the goals and mission of the organization, and the larger systems that organization is embedded in. That means you need a plan, and the plan needs to be periodically reviewed and communicated so that it stays in the forefront of peoples’ minds. And there needs to be a process for continuity of the plan when the people involved inevitably change over time. I don’t think embedding the plan only at the highest level of management works very well – it needs to be embedded at all levels of the organization, particularly mid-level management but even the rank and file, in my view.

accuracy of a model vs. its “decisional quality”

I like the way the abstract of this paper distinguishes between (1) the accuracy of a model as measured by comparing it to physical observations (always assuming those are an accurate or at least unbiased measurement of the true state of the universe and (2) the appropriateness of a model to be used in decision making. I find these concepts very, very difficult to get across even to scientists and engineers.

Ecological forecasting models: Accuracy versus decisional quality

We consider here forecasting models in ecology or in agronomy, aiming at decision making based upon exceeding a quantitative threshold. We address specifically how to link the intrinsic quality of the model (its accuracy) with its decisional quality, ie its capacity to avoid false decisions and their associated costs. The accuracy of the model can be evaluated by the [Greek symbol rho – I don’t know what they mean by this just from reading the abstract] of the regression of observed values versus estimated ones or by the determination coefficient. We show that the decisional quality depends not only of this accuracy but also of the threshold retained to make the decision as well as on the state of nature. The two kinds of decisional errors consists either in deciding no action while an action is required (false negatives) or to act while it is useless (false positives). We also prove that the costs associated to those decisions depend also both of the accuracy of the model and of the value of the decision threshold.

Ecological Modeling

September 2019 in Review

Most frightening and/or depressing story: Most hopeful story:
  • I think Elizabeth Warren has a shot at becoming the U.S. President, and of the candidates she and Bernie Sanders understand the climate change problem best. This could be a plus for the world. I suggested an emergency plan for the U.S. to deal with climate change: Focus on disaster preparedness and disaster response capabilities, the long term reliability and stability of the food system, and tackle our systemic corruption problems. I forgot to mention coming up with a plan to save our coastal cities, or possibly save most of them while abandoning portions of some of them in a gradual, orderly fashion. By the way, we should reduce carbon emissions and move to clean energy, but these are more doing our part to try to make sure the planet is habitable a century from now, while the other measures I am suggesting are true emergency measures that have to start now if we are going to get through the next few decades.
Most interesting story, that was not particularly frightening or hopeful, or perhaps was a mixture of both:
  • I mentioned an article by a Marine special operator (I didn’t even know those existed) on how to fix a broken organizational culture: acknowledge the problem, employ trusted agents, rein in cultural power brokers, win the population.

special operations culture

This article by a Marine special operator says special operations have a culture problem. That doesn’t surprise me too much. Anyway, here is the prescription the author gives for addressing an organization’s culture problem:

  • Acknowledge the problem. It’s hard to spot a slow change from within an organization. One solution is to have a peer organization do a review.
  • Employ trusted agents. These are sort of the blue collar leaders.
  • Harness and rein in the cultural power brokers. These are more like the middle management.
  • Win the population. This is an idea for counter-insurgency where you try to peel the bulk of the population away from a few bad actors within their ranks.

The article mentions “core values”. My own observation about core values is that strong, well-functioning organizations tend to already have them implicitly, and when you have to make a big deal about training people in them explicitly your culture is already lost. I’m not sure you can change individuals’ core values all that much. You can try to weed out people with bad ones and bring in people with good ones.

value of learning curves in climate change planning

This article gives an example of how to put an economic value on climate change adaptation incorporated in a larger planning framework.

The Economic Value of Climate Information in Adaptation Decisions: Learning in the Sea-level Rise and Coastal Infrastructure Context

Traditional methods of investment appraisal have been criticized in the context of climate change adaptation. Economic assessment of adaptation options needs to explicitly incorporate the uncertainty of future climate conditions and should recognise that uncertainties may diminish over time as a result of improved understanding and learning. Real options analysis (ROA) is an appraisal tool developed to incorporate concepts of flexibility and learning that relies on probabilistic data to characterise uncertainties. It is also a relatively resource-intensive decision support tool. We test whether, and to what extent, learning can result from the use of successive generations of real life climate scenarios, and how non-probabilistic uncertainties can be handled through adapting the principles of ROA in coastal economic adaptation decisions. Using a relatively simple form of ROA on a vulnerable piece of coastal rail infrastructure in the United Kingdom, and two successive UK climate assessments, we estimate the values associated with utilising up-dated information on sea-level rise. The value of learning can be compared to the capital cost of adaptation investment, and may be used to illustrate the potential scale of the value of learning in coastal protection, and other adaptation contexts.

gamification and water planning

This article is about gamification and water planning.

A review of water-related serious games to specify use in environmental Multi-Criteria Decision Analysis

Serious games and gamification are nowadays pervasive. They are used to communicate about science and sometimes to involve citizens in science (e.g. citizen science). Concurrently, environmental decision analysis is challenged by the high cognitive load of the decision-making process and the possible biases threatening the rationality assumptions. Difficult decision-making processes can result in incomplete preference construction, and are generally limited to few participants. We reviewed 43 serious games and gamified applications related to water. We covered the broad diversity of serious games, which could be explained by the still unsettled terminology in the research area of gamification and serious gaming. We discuss how existing games could benefit early steps of Multi-Criteria Decision Analysis (MCDA), including problem structuring, stakeholder analysis, defining objectives, and exploring alternatives. We argue that no existing game allows for preference elicitation; one of the most challenging steps of MCDA. We propose many research opportunities for behavioral operational research.

leading implementation of complex programs

This is just something I have wanted to write down a few thoughts on for awhile. My field is engineering and planning, and I have been involved in a number of programs that are complex technically, financially, and on the people side. I’ve seen some things done well, I’ve seen some things done badly, and I’ve done a few things well and learned a few lessons the hard way myself. So here are my thoughts:

  1. Organize the entire program around achieving a vision and set of goals which everyone understands. Create a crystal clear vision and set of goal statements for the program. Make sure these are thoroughly understood by all senior and mid-level decision makers – communicate, market, train, drill, test – whatever it takes to make sure they get it. Then, set specific objectives for individual functional units within the organization, and for all individual staff members, that advance these goals, all these goals and only these goals. Make each objective SMART – specific, measurable, achievable, realistic, and time bound. Then track every individual’s and every unit’s progress towards meeting the objectives, and hold individuals and managers accountable for meeting their objectives.
  2. Make sure the knowledge level of the entire staff is up-to-date with industry standards and best practices, then encourage system thinking, creativity and innovation to advance the leading edge. Create a formal training and continuing education program for staff. Create a psychological “safe space” for discussion of ideas that are outside the typical daily functions of the organization. Organize talks, discussion groups, and other events. Bring ideas and speakers in from outside the organization. Encourage and reward staff to spend time reading and attending events outside the organization, then bringing back ideas and communicating them to colleagues. Be on guard for the development of group think, and actively encourage and reward the sharing of ideas that are new to the organization.
  3. Focus on communication of system behavior, risk, and other complex information. Continuously improve staff knowledge of communication approaches, strategies, and tools by weaving these into the training and innovation program. Bring in specialized staff with communication and visualization skills. Set up a specific job role, group or committee whose job it is to oversee communication approaches in all aspects of the organization.

scenario analysis

Maybe this is not of interest to everyone, but I am always looking for new ways to analyze and communicate the results of alternatives and scenarios.

The diversity of socio-economic pathways and CO2 emissions scenarios: Insights from the investigation of a scenarios database

The new scenario framework developed by the climate change research community rests on the fundamental logic that a diversity of socio-economic pathways can lead to the same radiative forcing, and therefore that a given level of radiative forcing can have very different socio-economic impacts. We propose a methodology that implements a “scenario discovery” cluster analysis and systematically identifies diverse groups of scenarios that share common outcomes among a database of socio-economic scenarios. We demonstrate the methodology with two examples using the Shared Socio-economic Pathways framework. We find that high emissions scenarios can be associated with either high or low per capita GDP growth, and that high productivity growth and catch-up are not necessarily associated with high per capita GDP and high emissions.

infrastructure infrastructure infrastructure

Hillary wants to spend $250 billion on infrastructure. Bernie wants to spend $1 trillion. Infrastructure investment is good. Economists all say so. Politicians who care at least a little about reality all say so. The American Society of Civil Engineers says so. I happen to be a civil engineer so it is definitely good for me.

The idea is that the infrastructure we have in the ground now wears out gradually, and we almost never do enough maintenance to keep it in its original condition. So there is a constant loss of value to the economy. You can also think of infrastructure as reducing friction, transaction costs, wasted time, effort, and energy in the economy. If people and goods can get where they need to go efficiently and cheaply, they can do more productive work and produce more value each day with less waste. The same idea applies to getting energy and water around. Finally, there is the economic idea that when economic growth is below capacity, as it is now, you could pay people to dig holes and fill them back in again, and there would be a net economic gain. So any infrastructure investment, even if it is not optimal, has even more benefit and it is a sort of free lunch, something for nothing, two birds with one stone. And interest rates are so low that it makes sense for the government to borrow money, or even print it, and realize this almost automatic, guaranteed, magical return on the investment. Politicians who oppose infrastructure investment on debt or deficit grounds just don’t understand or don’t care about reality, or else they are telling a cynically calculated story to people who don’t.

Optimal, planned infrastructure investment would be even better, of course. We really don’t do any planning at the national scale in the United States. Maybe we are still trying to differentiate ourselves from the Soviet planned economy, but seriously, it is time to get over that particular hangover.

I like Elon Musk’s Hyperloop because it is a big idea and it asks if we should be considering something bold, big, and different, not to mention more efficient and cost-effective than the same old ideas from the 1950s! But we need to remember infrastructure is not just about transportation, it is about energy, water resources, food, commerce, trade, information, environmental quality and ecosystems. Infrastructure has an ecological footprint which needs to be measured and considered in decisions. Let’s think big, take a holistic, long-term look at the whole system, and ask what kinds of investments would be best. What kind of integrated infrastructure system do we think would make sense in the future, and what steps would we take today to get there? One of the hard things about infrastructure, though, is that it is long-lived. The technology available actually changes much faster than the infrastructure wears out, so blindly repairing and perpetuating old infrastructure ends up retarding the pace of change. A good plan needs to take a long-term view, but it has to be flexible enough to adapt to changing technology, environmental, climate and socioeconomic conditions during the course of its implementation. This is the essence of good planning, but it is hard for many of today’s hyper-specialized, local- and short-term-thinking professionals to pull off.