Game theory in action

Dapp Data
Dapp Data
Jul 26 · 4 min read

To-date, F1® Delta Time — an officially licensed F1 racing simulation game that will run on the Ethereum blockchain when it launches in early 2020 — has completed three auctions for high value, unique vehicles.

The first — for what was labelled the ‘1–1–1’ car — completed at 416 ETH (or around $110,000)

The second — a Monaco Grand Prix-themed car, ‘Monaco Edition’ — completed at 108 ETH (around $33,000).

The third — a French Grand Prix-themed car, ‘France Edition’ — completed at 97 ETH (around $25,000).

(Obviously the ETH/USD priced was fairly dynamic during this May to July timeframe.)

More interesting, though, is the attempt to see how the psychology of the individual bidders changed during these auctions.

Information asymmetry

The case of the first auction was clearly a situation in which no-one knew anything. No doubt developer Animoca Brands had aspirations of how it wanted the auction to proceed, but until the auction begin, this would have been mere conjecture.

What did quickly become clear, however, is that there were a number of high net worth individuals actively bidding for the ‘1–1–1’ car.

Notably the wallet labelled ‘robertwhite’ made seven bids, five of which increased the previous bid by 50%. This aggressive behavior quickly saw the auction total rising from less than $5,000 before ‘robertwhite’ started bidding to almost $100,000 when it stopped.

‘Robertwhite’s’ final (and seventh) bid was 360 ETH, worth $92,124. But neither this or other active wallets such as ‘Steve321’ and ‘DWM’ won the auction.

All were trumped by an anonymous wallet ‘09E282’, which made a single bid (each bid added five minutes on the auction period) and won ‘1–1–1’.

Second time unlucky

Given the great success of the first auction, the surprising thing about the second auction was how different it was.

In retrospect, this now seems obvious but as the second auction progressed, it was not the case. For one thing, coverage of first auction had raised the game’s profile; something that could be seen in terms of more wallets bidding. For another, all of those end game bidders from the first auction — ‘robertwhite’, ‘Steve321’ and ‘DWM’ — returned for another shot.

Surely there was a high probability the Monaco Edition car would sell for a similar, if not higher price?

Despite all these reasons, the second auction had a totally different dynamic.

No-one aggressively increased the bidding, with the exception of one 50% raise from ‘robertwhite’, and although there were more bids — 48 compared to 37 for the first — the final bid was a fraction of 416 ETH (26% in fact)

Interestingly, the winner this time was ‘Steve321’, who despite having bid 396 ETH as the highest losing bid for ‘1–1–1’, gained the Monaco Edition for 108 ETH.

Clearly, despite having many of the same participants who had access to the same funds in their wallets, the second auction was a very different beast.

In the first auction, three bidders drove up the price to a maximum just how an auction mechanic is designed to work. In the second, however, despite one 50% raise, there was no appetite to repeat the process.

The game theory had switched to one in which the participants realized they could take control to minimize, not maximize, the winning bid.

(As an aside, some people with which I’ve discussed these auctions assume there was collusion between bidders to drive the price higher. I haven’t looked at this question forensically but haven’t seen any evidence for this. Equally, the only parties who would benefit from this would be developer Animoca Brands and auction platform OpenSea, which took a 2.5% cut of the final bid. I know both companies fairly well and think such involvement highly unlikely, not least because Animoca Brands is a publicly-floated company (ASX: AB1) and such action would destroy its reputation.)

Finding consensus

The trend from the second auction was reflected in the third auction for the France Edition, which followed the pattern of the Monaco Edition almost exactly.

Many participants were the same, including ‘robertwhite’ who would once again end up as the losing highest bidder’, and ‘ogasahara’ and eventual winner ‘lovelili’, both of whom had been active in bidding for the Monaco Edition.

Indeed, if anything the bidding was even less aggressive; the average raise of the last 20 bids was 5% compared to 12% for Monaco Edition, and 28% for ‘1–1–1’.

Clearly by this time, market consensus in terms of pricing such assets had been reached. The first auction had been an anomaly, albeit one that was a massive success for Animoca Brands.

Are there any conclusions from this analysis? Perhaps.

  • For companies trying to sells such NFTs, their most valuable tool to maximize overall value is information uncertainty.
  • For participants, their most valuable tool to minimize overall value is to limit aggressively bidding.

Blockchain Gaming World

Everything worth knowing from the messy collision of blockchain and gaming

Dapp Data

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Dapp Data

Enlightened by numbers

Blockchain Gaming World

Everything worth knowing from the messy collision of blockchain and gaming

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