Dambisa Moyo offers a four-point strategy for weighing investment or policy choices during uncertain times

We are facing greater uncertainty – economically, geopolitically, socially – than at any time in recent memory. In these conditions, it is crucial for CEOs, policymakers, investors, and other decision-makers to draw on a wider set of data sources and sift through more signals of varying quality and reliability. Moreover, they must do this in a world that is absorbing the effects of deglobalisation, amid heightened international tensions and fast-moving technological developments like the diffusion of AI. Here and in other areas, private individuals and companies whose activities could affect all of society are operating with little regulation or oversight.

Decision-makers therefore must account for a variety of risks. Overwhelmed by the sheer volume of data and the depths of today’s uncertainty, they could simply do nothing; but that, too, is a decision with potentially significant consequences.

Those leading large organisations or setting policy also must be wary of relying on data or models that do not adequately reflect today’s changing macro, geopolitical, and market dynamics. Poor decision-making, misallocations of assets, or bad policy are particularly worrisome now that tail risks appear to be elevated. Under such conditions, the costs of any incorrect decision are likely to be amplified substantially.

Decision-making requires framing the problem correctly, surveying the terrain, updating data and information sources accordingly, and settling on a model to evaluate competing choices. Each stage of this sequence – what the military strategist John Boyd called the OODA loop (observe, orient, decide, act) – must be constantly revisited to reflect structural changes.

Framing the problem correctly implies clearly identifying your overarching goal. A corporation wants to maximise profit and shareholder value, just as a policymaker may want to maximise citizens’ living standards. In either case, decision-makers must identify and focus on those areas where they have actual control, such as resource deployment, cost-cutting priorities, or the overall strategic direction (asking, for example, which regions you should operate in).

The second task is to recognise the dynamics that are determining your operating conditions. The COVID-19 pandemic underscored the fact that the landscape can be far more complex than you might have initially thought. What was originally seen as a single-player problem with a set time horizon – as a health issue that would be resolved within a year once a vaccine was released – soon proved to be a multi-player problem with shifting time horizons.

From public health and the economy to educational and social settings, the pandemic affected all areas of civic life. Five years later, the world is still dealing with pandemic-related issues, including high government debt burdens, mental illness, and lower educational attainment.

Meanwhile, the trend toward deglobalisation has continued, altering the operating terrain for global business. Corporate leaders now must consider how to maximize their financial returns in a more siloed world, where the pillars of the globalised economy – free flows of goods, capital, and labor across borders, along with multilateral governance – are being eroded or even toppled.

Under these circumstances, many established business models are becoming riskier or obsolete. One can no longer assume an ability to hire international talent, maintain global procurement centers, borrow cheaply in London and New York, or invest in emerging markets and repatriate the returns.

Third, all leaders must regularly update their analytical tools. This imperative is even more urgent in the AI era. The task is not only to re-evaluate the breadth and depth of information sources, but also to address data-quality issues. The sheer scale and sophistication of AI-powered analytics means that decision-makers can and should go through the OODA loop much faster, such as when evaluating a prospective investment in a new country or considering the viability of a policy.

Lastly, how decision-makers evaluate competing options is critical. Many institutions use risk-mitigation strategies to determine how they should allocate resources. They may be guided by explicit regulatory mandates or by their own assessments of investment exposure under increasingly uncertain conditions. Thus, corporate boards more regularly use these strategies to protect asset values and adjust investment and capital-spending plans, perhaps setting aside contingency funds when the operating environment becomes more volatile.

Similarly, in financial markets, investors commonly use the Kelly criterion (a formula from probability theory) to determine the optimal bet or investment to maximize long-term wealth growth. Alternatively, the Minimum-Maximum Regret Theory (or Minimax) minimizes the maximum potential regret from a decision. Here, the decision-maker mitigates the potential loss (regret), rather than seeking to maximize returns given uncertainty.

In practice, all these methods offer a way to quantify a measure of success and a measure of risk, and then to understand the trade-off between the two values. But in times of increasing uncertainty, you should question whether your chosen method remains relevant or ideal. Decision-makers must recognize that failing to review how they weigh their options – trying to maintain the status quo – carries risks of its own.

Dambisa Moyo, an international economist, is the author of four New York Times bestselling books, including Edge of Chaos: Why Democracy Is Failing to Deliver Economic Growth – and How to Fix It. This content is © Project Syndicate, 2025, and is here with permission.