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Optimal Area Currency With Milton
Friedman and Mario are about the same height.

Friedman and Mario are about the same height.

In hindsight, one of Friedman’s great predictions is the Eurozone crisis. Despite being a massive champion for flexible exchange rates, Friedman never advocated for a common European currency.

Europe exemplifies a situation unfavourable to a common currency. It is composed of separate nations, speaking different languages, with different customs, and having citizens feeling far greater loyalty and attachment to their own country than to a common market or to the idea of Europe.

— Milton Friedman, The Times, November 19, 1997

The Greek financial crisis exemplifies many of the problems Friedman points out.

In an economic recession, central banks devalue the domestic currency to return the country to full employment. When economies are similar, recessions move together; if there’s a crisis in Texas, it’s probable there’s one in Washington. This makes central bank policy more effective because capital won’t escape from ‘recessed’ areas to ones with higher returns – there are none. It can be much harder to accomplish this in Europe, where every country’s economy is dramatically different, and institutional policy fluctuates widely.

What the hell does this have to do with game design?

Why do multiple currencies exist in games? Why not just have one type of currency rather than four or five?

The answer is segmentation and capital flight.

Once again, Supercell has provided us with a beautiful example, Clash of Clans (CoC). In CoC, some items cost gold, others and others elixir. After a quick scan, you’ll notice only the defensive items (cannons, archer towers, walls) cost gold, and only the offensive items (troops, barracks, spells). Why might this be the case? Segmenting these items gives designers greater control over the economy and minimizes the potential for ‘contagion’ effects. Consider a world in which Clash of Clans only contained gold. Players might prefer attacking rather than defending, encapsulating the idea of capital going to its highest return. If this were the case,e the game could become unbalanced as all players attack and none spend gold to upgrade their base defense. By segmenting base defense into the elixir, you remove any opportunity cost from expenditures for base defense. This is similar to giving your relatives a gift card; instead of spending it on whatever they fancy, they must now spend it on whatever is from the gift card store. A domestic currency is much like a gift card to that country’s ‘store’ just as an elixir is a gift card to only CoC’s offense ‘store.’

If Supercell finds players are not creating challenging defenses, increasing the rate of gold production is straightforward without worrying that money will be spent on offense. They can also do this by lowering the cost of items priced in gold. Supercell has toyed more with this strategy in their other title, Boom Beach.

The rules for when segmentation is worthwhile emerge from reading Mundell’s famous paper 1 backward.

Segmentation in games, like in real-world economies, gives game designers and central bankers more control.

  1.  Mundell, R. A.. (1961). A Theory of Optimum Currency Areas. The American Economic Review, 51(4), 657–665. Retrieved from http://www.jstor.org/stable/1812792
There’s More to A/B Testing Then A & B: I

One of the most powerful features of mobile games is the ability to run simultaneous randomized experiments at no cost. Academics swoon at such a possibility, and it’s very real and very spectacular in F2P games. Decades of running experiments in academic research can lend insight to developer scientists. An example is an insight from experimental economics called ‘bending the payoff curve.’

One of the favorite topics of experimental economists concerns risk aversion and auction theory, risk aversion due to its ability to challenge the neoclassical paradigm (i.e., mainstream economics), and auction theory because it uses fancy mathematics. The first groundbreaking economic experiments employed auctions in lab settings to see if participants diverged from rational behavior. A series of experiments run by Cox, Smith, and Robinson2 appeared to show participants were not doing what we’d expect them to do if they were rational agents (i.e., getting the lowest price). The suggestion being participants were acting as risk-averse agents rather than risk-neutral agents. However, the critical insight came from a challenge in how these experiments were run from a 1992 AER article called Theory and Misbehavior in First Price Auctions.3

The author, Glenn Harrison, argued that the costs of engaging in non-optimal behavior were minimal. In other words, being dumb didn’t cost participants much, and being smart didn’t earn participants a great deal either. Glenn argued this casts doubt on the suggestion that participants were engaging in non-optimal behavior. Still, instead, participants weighed the expected mental effort of being innovative and concluded it wasn’t worth the foregone increase in income.

Each deviation from zero (the optimal bid) costs the participant little.

Glenn argued that what researchers need to do was bend the payoff curve, i.e., increase the reward for being smart. This way, researchers can see if their testing behavior is accurate.

What does this mean for A/B testing in my game?

Developers often turn to A/B testing to test even the most minute items; frustration emerges when results are inconclusive. For example, Supercell might test whether players prefer reward schemes X or Y in Boom Beach by way of sessions played. An A/B test that presents each scheme after a battle could be inconclusive. This is because each reward has a small outcome on player progression. That is an insight, but if we’re interested in whether A or B is better, it will make sense to ‘bend the payoff curve.’ That means we’d offer A + 5 or B + 5 to exaggerate the effects of the different reward schemes.

Think of it as amplifying two lights on each side of a room to see where flies gravitate toward. If the lights were dim, the effect on the flies would be smaller than otherwise.

See? Just like an A/B test.

See? Just like an A/B test.

While not always appropriate, bending the payoff curve is another tool developer scientists should consider when designing experiments.

  1. Cox, James C., Bruce Roberson, and Vernon L. Smith. “Theory and behavior of single object auctions.” Research in experimental economics 2.1 (1982): 1-43.
  2. Harrison, Glenn W. “Theory and misbehavior of first-price auctions.” The American Economic Review (1989): 749-762.

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