Why Gamers Overpay for Loot Boxes: The Secret Behind Drop Rates

Overview

This paper explores how people respond to lottery-style products—items whose outcomes are determined by chance, such as video game loot boxes, mystery toys, or digital draws. It shows that the way these lotteries are presented can strongly shape what consumers believe about their chances of winning and how much they are willing to pay.

When sellers highlight rare big prizes or hide real odds, buyers often misjudge probabilities and overestimate their likelihood of success. As a result, they spend more money for the same product, not because the product has changed, but because the framing makes it seem more rewarding.

What Are Loot Boxes?

Loot boxes are virtual items in video games that contain a random selection of rewards. Players purchase or earn these boxes without knowing what they contain until they are opened. Each box may include common items, rare cosmetics, or even exclusive prizes, all determined by chance-based mechanics.

The concept mirrors traditional lotteries: players exchange money (or in-game currency) for a randomized outcome, creating both excitement and uncertainty. However, unlike a public lottery, the probabilities of winning valuable items are often hidden or vaguely presented, allowing presentation and framing to strongly influence perceived value.

While loot boxes add excitement and unpredictability to games, they also demonstrate how framing and probability presentation shape player perception—precisely the effect measured in the study.

Method

The researchers conducted a controlled online experiment involving 802 participants — evenly divided into 401 sellers and 401 buyers. Sellers decided how to frame a lottery, while buyers reported both their willingness to pay and their belief about the chance of winning. Real financial incentives ensured honest and realistic behavior.

• Each seller–buyer pair interacted over several rounds.
• Lotteries mimicked real-world random reward markets (e.g., loot boxes).
• Choices were financially binding, making results behaviorally reliable.

Framing Techniques Used by Sellers

The experiment revealed that sellers often manipulate how information about chances and outcomes is presented. Two major techniques — Censored Odds and Selective Feedback — make buyers overestimate their chances of winning and pay more. Below, each technique is explained with an illustrative diagram.

1. Censored Odds

Sellers hide or merge probabilities to make winning appear more likely than it is. Instead of showing every possible outcome (e.g., 1% jackpot, 11% medium prize, 88% small win), they might advertise a simplified “12% chance of something good,” which sounds appealing but hides the truth.

2. Selective Feedback

In this case, sellers only show lucky outcomes — highlighting winners and ignoring losses. Players see examples of “amazing wins” that make success look common, even though it’s statistically rare.

Together, these two framing methods create a false sense of fairness and excitement. Buyers perceive the game as generous and think their odds are higher than they are — leading them to overspend far beyond the lottery’s true expected value.

Main Results

The experiment produced a striking result: more than 80% of sellers chose a deceptive framing instead of a transparent one. This choice wasn’t random — it reflected clear strategic thinking. Sellers realized that when information was framed to look more appealing, buyers would overestimate their chances and pay significantly more than the lottery’s true expected value.

In contrast, only about 20% of sellers remained honest and presented probabilities transparently. These sellers earned less on average, but they tended to value fairness or disliked using manipulation, often reporting moral or ethical discomfort with misleading buyers. Others simply underestimated how powerful deceptive framing could be.

Why 80% Used Deceptive Framing

• Profit Maximization: Sellers quickly noticed that misleading framings dramatically increased buyers’ bids. By grouping probabilities or showing only big wins, they could make the same lottery seem much more valuable.
• Psychological Leverage: Framed information triggered buyers’ optimism bias — they believed they were “due for a win,” especially when examples of jackpots were made more visible.
• Competitive Pressure: Since other sellers were using deceptive framings, honest sellers risked losing buyers. The market dynamic pushed even neutral sellers toward manipulation to remain profitable.
• Immediate Feedback: In the experiment, deceptive framers earned higher payoffs after each round, reinforcing the behavior — “cheating” seemed to work and quickly became the norm.

Why 20% Stayed Transparent

• Ethical Concerns: Some sellers refused to mislead buyers, citing fairness or discomfort with deception. They preferred to win honestly, even at lower profit.
• Underestimation of Effect: A few didn’t realize how much framing could alter perception, assuming buyers would see through manipulative wording.
• Preference for Simplicity: Transparent framings were faster to prepare and easier to explain, appealing to sellers who valued efficiency over psychological strategy.

Impact on Buyers

Buyers exposed to deceptive framings believed jackpots were several times more likely than they really were. This false optimism led them to pay up to six times the true expected value of the lottery. Sellers exploited this overconfidence, adjusting prices upward while maintaining the illusion of fairness.

Overall, the findings demonstrate that profit incentives strongly favor manipulation. Once sellers observed that deceptive framing reliably increased revenue, it quickly became a market norm. Only those guided by ethics or skepticism resisted — showing how, in unregulated markets, misleading presentation can dominate through economic self-interest.

Why Do People Overpay?

The data from the experiment show that buyers systematically overpaid for framed lotteries — often offering four to six times the real expected value (EV) of the prize. This was not because they enjoyed risk, but because the way information was presented distorted their numerical understanding of probabilities.

In transparent lotteries, buyers could easily see the true odds of each prize and made near-rational estimates. However, once the same lotteries were framed in a “deceptive” way — through selective feedback or grouped probabilities — buyers miscalculated the likelihood of rare, high-value outcomes. This shift was measurable: participants reported subjective winning probabilities that were several times higher than the actual statistical chance.

Mathematical Mechanism Behind Overpayment

Each participant’s willingness to pay (WTP) was based on their internal “perceived value” of the lottery, which can be written as:

If the real probability of a jackpot is 1%, but the framing makes the buyer believe it is 10%, then the perceived expected value becomes nearly ten times higher. The buyer’s price offer (their WTP) therefore exceeds the true expected return. This mechanism alone — belief inflation — explains almost all observed overpricing behavior in the data.

Quantitative Findings from the Experiment

• Under transparent framing, average WTP was about 3× the objective expected value (EV). This overestimation can be attributed to small rounding errors or mild optimism.
• Under deceptive framing, WTP increased to 4×–6× the EV. Participants explicitly reported higher subjective probabilities of winning, even though the odds never changed.
• Statistical regressions in the paper confirmed that misperceived probabilities fully explained the price gap — no additional “risk-seeking” behavior was needed to fit the data.

The pattern is consistent with belief-driven overvaluation. Buyers are not behaving irrationally in the sense of taking pleasure in risk — rather, they are making rational decisions based on incorrect inputs. Once the probability is misperceived, their willingness to pay follows logically from the inflated perceived value.

The study therefore concludes that the cause of overpayment is informational, not emotional: deceptive framing systematically alters numerical beliefs, and those beliefs fully account for the inflated prices observed in the data.

Experimental Conditions

The paper tests four controlled environments to isolate how presentation rules change seller behavior and buyer beliefs. From left to right, conditions go from fully transparent to maximally manipulable.

Why this matters: Comparing these four settings shows that simple presentation rule changes can massively shift buyer beliefs and WTP. The “Both” condition yields the biggest distortion, explaining why most sellers picked it when allowed.

Implications

The findings show that belief distortions — caused by presentation, not by changing the product — drive overpayment. Below are practical takeaways for companies and rule-makers that follow directly from the paper’s results.

Bottom line: because the study shows that framing alone inflates beliefs and prices, the most effective and proportionate response — whether you are a business seeking trust or a regulator seeking fairness — is to make probabilities and typical outcomes explicit and avoid formats that hide tiny jackpot odds or amplify rare wins.

Conclusion

The experiment demonstrates a simple but powerful insight: how information is presented changes what people believe—and therefore what they are willing to pay.

When sellers frame odds in ways that highlight rare successes or hide tiny probabilities, buyers systematically overestimate their chances of winning. This belief distortion makes the same lottery, loot box, or random product appear far more valuable than it truly is—allowing sellers to charge up to six times its real expected value.

In contrast, transparent formats—those that display exact probabilities and typical outcomes—largely remove the bias. Buyers make more accurate judgments, prices move closer to real value, and both sides understand what is being traded.

The paper’s core message is not anti-randomness, but pro-transparency: the fairness and efficiency of markets for chance-based products depend less on banning randomness, and more on how it is shown to people.

In short: the study shows that clarity, not chance, determines fairness in markets for random rewards. Whether designing a game, a sales platform, or consumer regulation, transparency about odds is the key to informed and balanced behavior.