Melbourne Cup, World Cup, or Eurovision?
Forecasting is hard, and even harder if you do not know what kind of game is being played.
Forecasting the future is hard. It would be nice if you could just apply mathematical models that map from the past to the future. That is what most models do – and in physics and other physical sciences that makes sense. The models might be complicated to the point of intractability, but at least conceptually, past performance is a guide to future performance.
In economic systems, though, human factors matter to at least some degree. Where exactly the system sits on the spectrum of complexity depends on what time horizon you are forecasting and how much the outcome is shaped by human behaviour and judgement.
Study the form guide
Many outcomes are driven by physical processes where current state depends on the previous state. Forecasting these processes is a bit like betting on horse races. Studying the form guide – knowing the current situation and recent past performance – is a reasonable guide to the next outcome, or so I’m told. Betting on horse races or other non-human endeavours is still far from a sure thing. Random conditions matter: one horse might have a bad warm-up, or weather conditions might differ from the range seen over the period that your form guide covers. But physical reality is what matters, not least because horses do not collude amongst themselves.
There are circumstances where forecasting based on leading indicators works well, at least in the short term. Physical and decision-making processes induce process lags that predict future outcomes. Physical reality also helps determine other outcomes. For example, a country’s endowment of mineral resources and past investment in their extraction will determine whether that country is currently an energy importer or exporter, and so the impact of an energy price shock. Understanding these relationships can go a long way to supporting economic understanding more broadly, but it is not the whole picture.
Team of champions or champion team
Things get more complicated when groups of people are involved. Consider sporting competitions. You might have a team of talented players, but they need to train together and work well today. A poor playbook – the “operating model” of the team – will lead the team to underperform a better-organised team of lesser-known players. (Playing and training together in the same league – and mostly the same team, Barcelona – is, incidentally, one reason why the Spanish women’s soccer team does so well on the international stage. Examples in the current men’s World Cup competition, both positive and negative, are left as an exercise for the reader.)
Competition between businesses and conflicts between nations both fall into this category. The “form guide” of material reality, covering sizes of balance sheets or number of fighter jets, will not necessarily be a good predictor of the outcome. This is one reason some analysts misread the likelihood of a swift Russian victory over Ukraine. It was a category error to believe that the starting point of resources mattered more than the opposing side’s ability to out-organise and out-innovate. Some analysts were effectively forecasting as if it were a horse race, not a human competition.
The problem with forecasting in these environments is that it is much simpler to focus on what you can measure than to work out how people will react. Game theory can help here but quickly becomes intractable. It also presumes a lot about people’s priorities and preferences that might not be observable by a forecaster. Counting the fighter jets is so much easier.
This is an example of the broader point that we should always go beyond first-round thinking when forming our views. From higher participation by older workers swamping ageing of the population as a determinant of labour supply, to Saudi Arabia’s decision to truck oil to route around the Strait of Hormuz, people respond to their circumstances – including the human environment.
Pure Bangaranga
At least with a soccer match, the result is clearly defined: the team that scores the most goals wins. It is even harder when what you are trying to forecast is the result of a popularity contest. Consider Eurovision. Good songs and performances generally do well, but not always. ‘Jury panels’ of industry experts and the public both get a vote, and international sympathies matter, especially for the public vote. (This is why Germany and the UK tend to underperform most years, and Ukraine won in 2022. It is also why strong public votes have put Israel into second place both this year and last year.)
Financial markets fall into this category. At least in the short to medium term, forecasting financial prices such as equity prices or bond yields boils down to an attempt to assess what everyone else thinks about that asset and its prospects. This is essentially a popularity contest where people’s beliefs about the future matter, if anything more than their understanding of the present. In this environment, it does not matter what you think about AI, or US exceptionalism, or whatever other narrative has taken hold in the moment. It matters what everyone else thinks. The market can stay bonkers (or perhaps “Bangaranga”) longer than you can stay solvent.
Economics tries to handle this complexity by putting some structure around how people are assumed to form and update beliefs. Internal consistency, using all the information reasonably available, is the usual assumption here – also known as “rational expectations”. Other “bounded rationality” approaches consider the cost of obtaining or processing information, or the lags involved in learning about the world. The profession has not yet settled on which of these “bounded rationality” approaches should be the standard. This is a shame, because these models have strong implications for macro policy.
Reality matters in the end
The lesson for forecasters is to know what kind of game you are forecasting. This depends partly on the time horizon and partly on the nature of the thing being forecast. The longer the horizon, the more so-called fundamentals cut through. How people react will still matter, though, and can even shape the future path in a lasting way. Always allow for second-order responses.
When thinking about the outlook for interest rates, your forecasts for inflation, the labour market and so on serve as a kind of material reality benchmark. But what matters in the short run is how the policymaker will interpret and respond to those outcomes. That might not be how you would respond to the same information if you were in their shoes. There is a role for judgement in policy decisions, which means you are forecasting someone else’s judgement. A bit like predicting Eurovision, but with a lot less glitter.
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Australia and NZ Weekly 15 June 2026
Australia and NZ Weekly 15 June 2026
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