Category: Making Decisions under Uncertainty

Developing Intelligence for a Complex World

THE ORACLE’S SECRETS TO PRODUCING BETTER FORESIGHT

We’ve always assumed oracles of the future got their answers from breathing mountain air or from the gods looking down. The reality is the good oracles were canny oracles that did their homework first before delivering their fateful responses. They had access to great intelligence networks to help them, and most importantly before they gave a foresight response to a tough question they purposely disturbed the dynamics of the situation using quick probes to see what information would be revealed. We can’t look at a used car and expect to know how it will run; we have to kick the tires, turn on the engine, and give the car a test ride to have any sense of the future. Good oracles understood they weren’t passive participants in how the future played out; they were active, engaged ones. Whatever they said or did could influence the final outcome. Before delivering their foresight response, which was irreversible, they probed or stimulated the complex environment to see what possible behaviors could develop.

In modern day organizations, oracles have been replaced by effective intelligence practices that focus on getting “intelligence from the trenches,” conducting as much experimentation and testing in the market as possible, and learning quickly from emergent behaviors in the market. Google and Amazon have created amazing probing or active-intelligence capabilities that should enable them to remain very competitive for the foreseeable future.

INTELLIGENCE NEEDS FOR COMPLEX ENVIRONMENTS

Real-world environments are very messy. They evolve in nonlinear ways and are largely unpredictable. Even with a computer, we barely understand how the many forces interact and behave. We can have voluminous information about a situation, but it’s always very incomplete, often inaccurate, and very hard to integrate and analyze quickly. We generally have limited insight about the individual forces shaping the situation and the situation as a whole, and only understand in retrospect why things happened. We need intelligence practices that will help us better understand the situations we’re facing, that will coax out better foresights about future threats and opportunities, and that will enable us to act effectively in response to the changing environments.

Our intelligence needs start with understanding better the dynamics of complex environments. Real-world environments are complex because many interrelated forces interact unpredictably. Change in one type of force, social or economic or physical, inevitably affects the others, but the unfolding behavior of the system cannot be predicted by understanding the individual forces or any set of force interactions. Importantly, every stakeholder in a situation is a force, playing a role and acting on the other forces.

A complex environment is simultaneously dynamic and resilient. Resilience is the capacity of the environment to absorb disturbance, to undergo change, without crossing some threshold to chaos and a different dynamic. This capacity to undergo some change without a radical change in general dynamic is defined as the resilience of the system. The more resilient the system, the more anti-fragile (a term from Nassim Nicholas Taleb, the author of The Black Swan and Antifragile: Things That Gain from Disorder) it is.

The complex social-physical system becomes unstable and chaotic when changes in the interlinked forces result in thresholds being crossed. Shocks and disturbances to a system, such as from a natural disaster, technological disruption, etc., can push a system across thresholds into a different state or dynamic, often with unwelcome surprises. An accumulation of nonlinear changes can also push a situation over thresholds. Eventually the system reconfigures into the new dynamic and state with new thresholds.

Complex environments can be defined at many levels or scales. The highest level is the Earth system, but any real world issue, like the copper commodity market, the urbanization of Southern California, the development of renewable-energy sources in Europe, or migrations in Europe, can be defined as a complex situation.

Facing complex situations creates a number of strategy and intelligence challenges for organizations.

  • First, because real world situations are interactively complex and non-linear, they are difficult to explain, let alone predict some cause and effect. A relatively minor action like publicizing a foresight of the future can create disproportionately large effects. When Elon Musk makes a market prediction, he can have a large impact. However, the same prediction for the same issue at a later time may produce a different effect.
  • Second, each situation is unique and novel. Historical analogies can provide useful insights on individual aspects of the larger issue, but the differences among even similar situations can be profound and significant. The political goals at stake, the stakeholders involved, the cultural milieu, the histories, and other dynamics are unique.
  • Third, a complex situation can’t be known, only surrounded. The organization’s understanding of the issue depends on who’s involved, and each individual will see the relationships between the forces driving the situation and their importance differently.
  • Fourth, every description of the issue points in the direction of a set of foresights. The description puts blinders on what we see as foresight. For example, if one describes bankrupt commodity producers as the result of falling demand and lower commodity prices from a weak economy, the foresight of the future will be different than if we describe bankrupt commodity producers as the result of building too much supply capacity.
  • Fifth, any stakeholder’s action, including its intelligence activities, can disturb a situation with non-linear effects.
  • Six, real-world situations have unlimited possible outcomes; there’s no fixed set of possibilities. Also, there’s no way of knowing if many of the foresight possibilities have been identified and considered.
  • Seventh, foresight ideas for an issue are better or worse, not right or wrong. The suitability of a foresight and its perceived quality will depend upon how individual stakeholders have understood the situation and what constitutes success for them. The perceived quality of a foresight can change over time; yesterday’s foresight might appear good today, but disastrous tomorrow.
  • Eighth, every foresight for a real-world situation is a ‘one-shot operation.’ The interactive dynamics of a situation are continuously creating a new situation and cannot be undone. The consequences of change are effectively irreversible.
  • Ninth, real-world situations have no ‘stopping rule’. It is impossible to say conclusively that a situation has been resolved. Work will continue on an issue until strategic leaders judge the situation is “good enough,” or until stakeholder motivations, will, or resources have been diverted or exhausted.

ORACLE SECRETS FOR COMPLEX ENVIRONMENTS

Based on these challenges, we can surmise what were the secrets of the good oracles. How did they come up with good foresight for the tough questions of the day? There were seven things.

First, good oracles did their homework before coming up with a foresight response to a tough question posed to them.

Second, good oracles had intelligence teams to assist them. An oracle must develop a superior ability to identify signals of change from the external environment and see the new possibilities for all the players. The oracle needs a team to accomplish this and the team must specialize in watching complex situations—watching non-linear dynamics, emergent behaviors, etc., identifying the key uncertainties and the ranges of possible outcomes, spotting signals of change, gathering new data, managing probes in intense environments, and developing new insights on threats and opportunities. Oracle team persons need to have an entrepreneurial mindset to operate in those fluid situations, work with the open network, and communicate their insights upward. They must be able to recognize new patterns in a changing environment, know which types of relationships within the network are crucial at specific times, and mobilize relationships in order to accomplish objectives.

Third, good oracles developed a wide network of sources in the field, a network of players well beyond traditional players and boundaries.

Fourth, good oracles recognized they’re not outside observers in unfolding dramas, but active players in those dramas. Like every other player, an oracle action—particularly its foresight response—can shape the situation’s dynamics and possible outcomes.

Fifth, before settling on a foresight response, oracles first stretched their teams to identify the full range of possible dynamics in a situation and outcomes. The teams did this by focusing their intelligence activities on the big uncertainties, developing an understanding of the conditions from which opportunities or threats could emerge, and identifying the threshold boundaries—the tipping points—beyond which the possible outcomes and dynamics would change in chaotic ways. Very often the big uncertainties would be about the various players, who could emerge, how anyone might behave, etc. But there were many other possible big uncertainties—technology innovations, the success of new products and services, and local government rules.

Sixth, oracles extensively used probes. Instructive patterns can emerge from complex dynamics, if one can disturb or probe the situation and watch the effects. Probes can be a field test of a new product, an external-stakeholder interview, a publication of a blog, the posting of something to sell on eBay, etc. The objective was to coax out information about a major uncertainty, particularly about how key players might behave, so the oracle team could develop a better foresight response. Most probes will fail to produce anything, so oracles need a portfolio of them to create the opportunities for informative patterns to emerge.

 

Finally, good oracles made most of their money from retainer services because real-world situations rarely got resolved and clients wanted timely foresight updates. In fact, clients needed the oracles’ coherence in the midst of all the change, for seeing how the situational players were learning and adjusting to the changing dynamics. So good oracles developed processes for ongoing development of their network, watching of the dynamics of a situation, and probing the situation to stimulate new intelligence, if necessary.

HIGH TECH’S RELIANCE ON PROBES

In modern day organizations, oracles have been replaced by effective intelligence practices. Twenty years ago, Richard Pascale, former McKinsey consultant and Stanford Business School professor, described in a Sloan Management Review article, “Surfing the Edge of Chaos,” a set of strategic principles for organizations operating and competing in complex ecosystems and how Royal Dutch/Shell was attempting to apply those principles. One key principle was that in a world constantly evolving in ways one can’t predict, where one has a limited ability to understand the world and shape events and outcomes, organizations succeed best not by trying to control an unpredictable environment but by constantly disturbing it. Another principle was that decentralized organizational units were best positioned to develop the intelligence and insight for responding to the changing environment that was changing often in non-linear ways. At the time of the article, Royal Dutch/Shell was implementing a new management system that would rely on “intelligence from the trenches,” involve as much experimentation and testing in the market as possible, concentrate on rapid learning, and implement continuously adapting action plans. That new management system of Royal Dutch/Shell characterizes the approach many fast-growing corporations use today.

Probes are the techniques for generating intelligence from the trenches—by making small disturbances—and conducting market experiments and tests. They are the product tests, product announcements, market experiments, and interviews designed to get stakeholder responses. Probes are the means for resolving the uncertainties about what the stakeholders might do in the future. Will customers buy this new product idea? How might regulators respond to the new product or service? How might suppliers and competitors respond? Probes can be used to reveal emergent strategies of new entrants.

Online environments have totally changed how companies conduct probes. For example, the use of online surveys and tools like Survey Monkey has transformed how consumer research is done around the world. Corporations that compete in online industries and have access to millions of online users or customers are creating significant competitive advantages for themselves through their probes. They can run many probes, quickly, for little cost and are leveraging that capability to build their new products or services. Probes are a key for leveraging big data.

A major strategy of online companies is “ship and iterate.” This is essentially a strategy that leverages probing skills to commercialize a new product or service. The company doesn’t focus on getting a perfect first product introduced online but instead they initially make available online a close-enough product and then focus on iterating quickly to get improved versions into the hands of users. That first product shipment is in effect a big probe and generates a lot of useful information even if the product fails.

Google finds some of the most important data from the product-shipment probe is the negative feedback because it’s so motivating to the product development team. Google also believes in soft launching new products—i.e., only providing minimal marketing and public relations support with the initial launch, forcing the new product to gain momentum and succeed on its own.

Amazon believes speed matters with new products or services and, when there’s uncertainty about what might happen, it just tries something and takes advantage of the opportunities stimulated by doing something first in the marketplace. Being first also attracts to your product or service the critical segment of users and customers that is strategic and risk-taking—the innovators and generates for you the first feedback information from the marketplace that no one else will have. This bias for action is a characteristic of Amazon’s culture that is focused on continually trying to improve customer experiences.

For online and software companies, it’s easy to ship a new product or version. For hardware companies, it’s a little more difficult, but cost-effective probes can still be created. Online environments have enabled for hardware companies an array of new approaches and technologies for generating and testing product and market ideas fast, at low cost, and with not-much risk. For example, one can ship the design of the hardware, or one can create a virtual model that users can play with.

Question for the CEO: How Could You Pursue Short-Term Gains in a Cyclical Industry?

Doug Oberhelman, Chairman and CEO of Caterpillar, last week announced he would step down as CEO because of questions about the company’s future after four years of revenue decline. A Wall Street Journal article on October 17, 2016 noted that at a mining trade show in September Oberhelman said, “Everybody was surprised by the size of the downturn [in the commodities markets] and the length of it. I firmly believe we couldn’t have forecast that at the time.” No, Caterpillar shouldn’t have been surprised. How could any company dependent on a cyclical commodity industry be surprised by plausible cyclical developments?

When Caterpillar made a slew of major investments from 2010 to 2013 at the height of the commodities boom, I have to surmise they went after potential short-term gains and ignored the risks of a commodities slump. It’s also foolhardy for a company to make strategic investments in a very dynamic, constantly changing marketplace that don’t enable the company to adjust when very plausible changes begin to occur?

There are two strategy imperatives for companies competing in dynamic, complex markets: First, in evaluating the major strategic opportunities to pursue, explicitly evaluate the range of possible long-term developments the company could face. Second, in crafting a strategy, incorporate the ability to adjust the strategy as events and developments unfold—after you have a lot more information about important external decision factors. In other words: 1) you can’t predict the future, so don’t commit to a strategy that only succeeds under one set of assumptions, and 2) commit to a strategy that enables the company to act quickly in response to big changes when they begin to occur.

Another Wall Street Journal article last week on October 18, 2016 was about Ford Motor Co.’s new dual-track strategy to be an auto manufacturer and a transportation-services provider. On the surface, this dual-track strategy appears to satisfy the second imperative above. It would give Ford the ability to move or accelerate in different directions, depending on how events in the future unfold. What will be the markets for electric and self-driving vehicles and what business models will serve the new transportation landscape best? In effect Ford is saying, we don’t know what strategy will be the winning strategy, so we’re going to invest in a number of initiatives and develop the capability to move quickly to realize whatever big opportunities emerge. Continuing the One Ford strategy implemented by the CEO Mark Field’s predecessor would be too risky given the range of marketplace developments that could occur.

Implementing Ford’s dual-track adaptive strategy won’t be easy. It depends on building a decision making process that will enable the company to adjust its strategy—quickly if necessary—in response to unfolding events. The strategy needs to be dynamic, and this will require a shared—not just top down—decision making process, a new information gathering and assessment system, and a fast organization-pivoting capability.

The biggest obstacle in many companies to developing an effective strategy for an uncertain environment is senior executives’ unwillingness to use planning-under-uncertainty techniques like scenario planning for major decisions. That unwillingness mostly stems from a lack of knowledge and experience with the techniques, and the fact few multinationals are known to rely on them in making major decisions. That’s a shame because decision-focused, scenario-planning methods are mature, practical, and effective in creating strategy that everyone can get behind. They require some effort to learn, but that’s a small price if the result is the ability to make good strategic decisions in the face of uncertainty.

If the senior executives of an organization don’t use scenario planning or something like it, then the board of directors should insist on independently applying a stress test—like the too-big-to-fail financial institutions must undergo each year—to see how well the strategy and proposed investments of the organization would do in the face of major change.

Aha Insight from the US Army: Strategy for Complex Environments

Strategy/policy approaches are often inadequate for figuring out what we should do because they ignore reality. The world is uncertain, but we try to plan based on what we believe is certain. We should be planning based on hypotheses that the world operates like a complex adaptive system, everything is always changing, and most things about the future are uncertain.

A complex adaptive system is a good model for how the real world operates. Like the real world a complex adaptive system is constantly changing, but not changing in a predictable, linear, incremental fashion. When faced with a real-world situation—a situation that is hard to describe because of poor-quality information, many interconnections, and many uncertainties—we can start with a framework for complex adaptive systems and apply a strategy/policy decision process that will enable us to make some sense of a complex, dynamic situation, understand the limits of that sense, and generate good strategies for the situation. And because we’re dealing with a dynamic system, we need a process that will accommodate the change that will be continuous and prepare us to respond to that change as necessary.

New US Army Doctrine for Complex Environments

One very interesting approach put forth for achieving goals in a complex adaptive system is the proposed management doctrine of the US Army for designing and executing military operations in complex operational environments, like insurgency situations in Afghanistan and Iraq. The U.S. Army Capabilities Integration Center, Training and Doctrine Command, United States Army recently described that doctrine in Commander’s Appreciation and Campaign Design, Department of the Army TRADOC Pamphlet 525-5-500.

As outlined in the US Army’s pamphlet, complexity is significant to military commanders because it’s a basic characteristic of operational problems. The military defines an operational problem as a discrepancy between the state of affairs as it is and the state of affairs as it ought to be that compels military action to resolve that discrepancy. The complexity of operational problems ranges from tame, well-structured problems to those that are extremely complex and ill-structured. Unfortunately, most management doctrine today in the military—as well as in civil-government service and private corporations—is for well-structured problems hence the need for a different doctrine and the understanding for when to apply it.

Well-structured problems are controlled through technical reduction and a systematic method-based solution. They are easier to recognize and characterize. Most modern tactical doctrine of military services fits this mold, specifying the tasks, conditions, and standards for every task in warfare from tank gunnery to conducting a defense. The most structured problems often have just one correct solution, and success requires learning to perfect the established technique.

Medium-structured problems are more interactively complex, and while there is no single correct solution, personnel will agree on the structure of the problem, appropriate tasks, and the end state, but may disagree about how the general principles in doctrine are applied on a specific piece of terrain against a specific enemy. In a medium-structured problem, it is possible for a defense to succeed against one enemy commander yet fail against another under precisely the same circumstances. The difference between success and failure in this case is a function of interactive complexity, rather than a structural or technical difference between the two

In planning for a well- or medium-structured military situation, personnel will focus on the linear phenomena rather than the non-linear. They will focus on the practice of war, which is based upon professional consensus and is authoritatively prescribed in doctrine, rather than the art of war, which is based upon intuition and genius. Leader development processes are not designed to produce geniuses because geniuses are idiosyncratic. Instead, leader development processes are based on previous experience and practice and the linear phenomena that can be controlled and on whose structure personnel can agree.

Ill-structured (also called wicked) problems require a completely different orientation. Ill-structured problems are interactively complex, non-linear, and chaotic—and therefore the most challenging. Unlike well- or medium-structured problems, smart people will disagree about how to solve an ill-structured problem, what should be the end state, and whether the desired end state is even achievable.

A number of challenges need to be overcome to address an ill-structured problem.

  • The first challenge is that at the root of the lack of consensus about how to solve an ill-structured problem is the difficulty in agreeing on the structure of the problem. Unlike medium structured problems, it is not clear what action to take, because the nature of the problem itself is not clear. There’s not even a definitive way to formulate an ill-structured problem. For an ill-structured problem, the information needed to understand the problem depends upon how one defines it. And the solution depends upon how one understands the problem, or how one answers the question: “What is causing this problem?” Ill-structured problems rarely have a single cause, and different stakeholders will see the relationships between the causes and their importance differently. Thus, understanding and formulation depend to some degree upon the perspective of the problem-solver rather than some objective truth. Thus an ill-structured problem cannot be known, but must be surrounded.
  • The second challenge in addressing an ill-structured problem is one cannot understand an ill-structured problem without proposing a solution. Understanding the problem and conceiving a solution are identical and simultaneous cognitive processes. For example, if one describes bankrupt commodity producers as the result of falling demand and lower commodity prices from a weak economy, our solution will be different than if we describe bankrupt commodity producers as the result of building too much supply capacity. The formulation of the problem points in the direction of a particular solution.
  • A third challenge is every ill-structured problem is essentially unique and novel. Historical analogies can provide useful insights for individual aspects of the larger problem, but the differences among even similar situations are profound and significant. The political goals at stake, the stakeholders involved, the cultural milieu, the histories, and other dynamics will all be novel and unique to a particular situation.
  • A fourth challenge is that ill-structured problems have no fixed set of potential solutions. Since each ill-structured problem is a one-of-a-kind situation, it requires a custom solution rather than a standard solution modified to fit circumstances. For well- and medium-structured problems, best practices offer standard templates for action, standard ways of doing things that have to be adapted to specific circumstances. There is no similar kit of generic solutions for ill-structured problems. The dynamics that make an operational problem unique also demand the design of a custom solution. Additionally, there is no way to prove that all solutions to an ill-structured problem have been identified and considered.
  • The fifth challenge is that solutions to ill-structured problems are better or worse, not right or wrong. There is no objective measure of success and different stakeholders may disagree about the quality of a solution. The suitability of a solution will depend upon how the individual stakeholders have formulated the problem and what constitutes success for them.
  • The sixth challenge is that ill-structured problems are interactively complex. Operational problems are socially complex because people have tremendous freedom of interaction. Since interactively complex problems are non-linear, a relatively minor action can create disproportionately large effects. The same action performed on the same problem at a later time may produce a different result. Interactive complexity makes it difficult to explain and predict cause and effect.
  • The seventh challenge is that every solution to an ill-structured problem is a ‘one-shot operation.’ Every attempted course of action has effects that create a new situation and cannot be undone. The consequences of direct action are effectively irreversible. Whenever actions are irreversible and the duration of their effects is long, every attempted action counts.
  • The eighth challenge is there is no immediate and no ultimate test of a solution to an ill-structured problem. The perceived quality of a solution to an ill-structured problem can change over time; yesterday’s solution might appear good today, but disastrous tomorrow as the unintended effects become clearer. Measurable results to a particular action may not appear for some time. This time lag complicates assessment enormously, because in the meantime the operational command may have executed other actions, which will make assessing cause and effect even more difficult.
  • The ninth challenge is that ill-structured problems have no ‘stopping rule’. It is impossible to say conclusively that such a problem has been solved in the sense that a student knows when she or he has solved a math problem. Work on an ill-structured problem will continue until strategic leaders judge the situation is “good enough,” or until stakeholder motivations, will, or resources have been diverted or exhausted.
  • The tenth challenge is that every ill-structured problem is a symptom of another problem. The causal explanation for a problem will determine the range of possible solutions. Yet, solving one problem often reveals another higher-level problem of which the original one was a symptom. The level at which an operational problem is solved depends upon the authority, confidence, and resources of a particular commander. One should not simply cure symptoms, but should rather strive to solve the problem at the highest possible level. However, if the problem is formulated at too high a level, the broader and more general it becomes and therefore the less likely it is to solve particular aspects of the specific problem.
  • The eleventh challenge is that the problem-solver has no right to be wrong. The writ of an operational commander and his staff is to improve the state of affairs as his superiors perceive it. Like others in senior positions of an organization, he is responsible for the consequences of the actions he generates.

Given these challenges facing military leaders, the process for confronting an ill-structured problem—for trying to have a healthy future in a complex adaptive system—has to be very different from how situations were generally approached in the past. The US Army pamphlet identifies several key features for a new approach:

  • Shared development of plausible scenarios. The task of defining the problem will require much more work and insight than before, and it’s not something that can be done top down by the commander. Instead, given the uncertainties and complexities of future situations, commanders must approach the problem with a holistic systems perspective using both bottom up and top down inputs. Ultimately, developing a shared understanding of the external environment situation is a critical success factor in defining the problem and quantitative models won’t be very useful. Instead, qualitative, heuristic approaches will be needed to create a shared understanding of the circumstances and possibilities. Based on my experience, a very good holistic approach for creating a shared understanding of the problem or challenge, and one that recognizes the problem or challenge is going to evolve over time with the actions of the participants and unfolding dynamics, is the scenario development process.
  • Shared strategy decision-making. Since each strategy and action solution will be a function of the shared understanding developed for the particular problem or challenge, a top-down developed solution probably wouldn’t work. The process for developing a solution should involve all the key stakeholders and utilize the scenarios.
  • Prepare for continuous change. Since a critical feature of insurgency conflicts is how rapidly the situation or problem changes over time, all participants are in an unrelenting struggle to learn and adapt rapidly, and they do. Over time the original shared understanding of a problem or challenge will no longer be valid and will need to be changed, resulting consequently in the need for an adjusted strategy and action plan. Organizations will need:
    • A continuous process of strategy, with abilities for ongoing monitoring, assessing, and making adjustments to the action plan;
    • Companies and squads closest to the action in the field need the responsibility and authority to conduct that process and make the adjustment decisions. The best information and awareness of the changing situation is in the field and there’s often not enough time for those in the field to brief those at the top and involve them in a process to develop a suitable response;
    • Individual squad members to be utility players more than specialists, able to play multiple roles as needed. New task responsibilities of the squads are assigned to those best suited to carrying them out. The activities of individuals will shift as required by the circumstances.

Implication for Policy and Strategy Development for Global Situations

If this Army doctrine makes sense for complex operational environments, then private corporations, local government agencies, and community planning committees should all use similar principles when developing strategy. But instead they employ processes that are linear and top down and don’t have a good chance of succeeding. They focus on the certainties rather than the uncertainties; they look at issues in isolation rather than being part of an interconnected environment; they forecast or assume one future rather than anticipate a range of plausible futures; they assume a best solution can be found; and they don’t plan for the inevitable change in the future after implementation begins.

First Blog Post: Global Futures Framework

My name is Bill Ralston. This is a blog of hypotheses about the future based on emerging trends and signals of change. After many assignments developing scenarios of the future for businesses and government agencies, I get to present my views on key forces shaping the globe. I will often focus on commodities—ranging from agricultural products, to minerals and metals, to oil and gas, to fresh water—that drive economic development of emerging economies and are a major factor in the politics, economics, changing physical environment, and social priorities of developed economies. Few appreciate how dynamic Mother Earth is and how extreme the shifts in supply capacity, commodities demand, and resultant money flows can be. Economists, government leaders, and business interests continually overlook commodities’ importance and are often surprised by the wrenching turns of the markets. They simply don’t have good mental models for how commodities markets operate in our complex world and can’t appreciate the range of plausible outcomes they could be facing in both the near- and long-term futures.

In my business life I worked at Brown & Root, SRI International, and Strategic Business Insights. I started off as an engineer, helping design and build oil and gas facilities around the world. I then consulted to energy companies and civil infrastructure operators on strategy, technology, and business issues; I moved into consulting on what businesses and government agencies should do about environmental, health, and safety issues; and eventually found a role where I could integrate all my experiences: I became a corporate futurist, helping management teams develop scenarios of the future for the major issues they were facing. Throughout all of this, I was learning about decision-making under uncertainty, risk management, models of complex environments, and making projections about the future.

In this blog, I want to develop a global futures framework that will enable better insights about challenges the world faces. I want to apply this framework to a host of emerging issues and identify future plausible outcomes that businesses, governments, and societies could face. My analysis template will be (i) hypothesis about future threat or opportunity, (ii) recent signals of change, (iii) plausible outcomes in the future, (iv) implications, and (v) what to watch for in the future.

Emerging issues that are on my agenda to address include ocean-development opportunities and risks; Latin America economic-growth prospects; opening up of the Arctic; commodities dependency of emerging economies; civil infrastructure needs; global agricultural supply; petroleum in the future; climate change policies; Africa and China; Russia and the United States: the world’s largest commodity suppliers; the importance of technology innovation in commodity industries; European-nation energy strategies, etc.

Thank you for reading.