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Loss Aversion
Loss Aversion
Loss Aversion
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This mental model validates the old expression, ‘a bird in the hand is worth two in the bush’. In fact, researchers found that when betting on a coin toss, participants were consistently motivated to bet on the 50/50 outcome when the potential gain was more than two times greater than their potential loss. 

Loss Aversion is a cognitive heuristic where ‘losses loom larger than gains’. 


This model means that the pain of a loss will be much more significant than the satisfaction from an equivalent gain. Win $100 on a raffle and you’ll be happy, but get fined $100 for speeding and, according to this model, you’ll be disproportionately upset. 


Loss Aversion will hold a special place in the hearts of many behavioural economists, as it was one of the early findings uncovered by researchers Tversky and Kahneman that informed Prospect Theory and underpinned their approach as captured in the Fast and Slow Thinking model. 


Understanding Loss Aversion helps you to influence behaviour in a variety of contexts. Want someone to take a risk or motivate them into action? Try to connect with what they might lose rather than what they might gain. By the same token, understand how the fear of loss might lead to risk-averse inertia unless the challenge can be reframed. 

Specifically, this model can inform: 

  • Sales commission structures: pay commissions upfront then deduct when milestones aren’t met.

  • Product and marketing strategy: by offering free trials or focus on discounts over returns. 

  • Behaviour change: using Commitment Contracts, see Actionable Takeaways for more on that.

  • Change management and communications: reframing perceived losses. 

See the Actionable Takeaways below for more on these approaches. 

It's a useful model but also be aware that there have been inconsistent results in some studies relating to Loss Aversion — see Limitations below for more. 


This model both connects with and helps to explain the Endowment Heuristic, of overvaluing things that you already possess, and the Sunk Cost Fallacy, of not being able to let go of past losses. In that sense, it also helps to explain the Lock-In Effect, where customers display inertia and remain with an existing service despite seemingly better alternatives.

The Regret Minimisation Framework is a way of tapping into Loss Aversion and challenging you to ‘live into’ your Opportunity Cost, focusing on potential regrets to help give you the confidence to take bold action. 

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Actionable Takeaways
  • Consider using penalties over rewards. 

Your instincts might lead you to focus on rewards as a positive way to encourage motivation, yet the evidence behind Loss Aversion would say otherwise. Research associated with this model shows that focusing on potential loss and using ‘penalty frames’ can be more effective to encourage behaviour change. 

  • Ask ‘what’s the cost of doing nothing?’

A new initiative designed to deliver gains can still focus people on what might be lost. One way to counter this is to ask ‘what’s the cost of doing nothing?’ This focuses attention on the potential Opportunity Cost. The Regret Minimisation Framework model takes this to another level. 

  • Use a Commitment Contract. 

A Commitment Contract is perhaps best demonstrated via the Stickk example under In Practice below. It’s a commitment where you define a desirable goal; you select a third-party referee, and you choose something that you will lose if you don't succeed. This penalty might be donating money to a cause that you hate, a self-imposed fine or any form of loss. 

  • Pay conditional bonuses upfront to motivate behaviour. 

Rather than offering a sales commission, reward or bonus after a certain milestone is achieved, consider prepaying the bonus and then taking it away if the desired milestone is not reached. I know, it seems cruel, but it works.

  • Minimise perception of losses and reframe as ‘fewer gains’. 

See the In Practice section below about how pensions were structured to minimise the perception of loss by linking them to pay raises. Consider how you can reframe any conversation about potential losses as a reduction in gains instead. 

  • Beware/use free trial periods. 

A free trial period essentially gives something to someone, then threatens to take it away when the free period is over. Rather than assess this as ‘do I want to have this?’, you will be confronted with ‘do I want to lose this?’ which, with your understanding of Loss Aversion,  you know will tilt the balance in the provider's favour. 


There are a number of studies, including the original ones by Tversky and Kahneman that provide compelling evidence behind Loss Aversion. However, there are also exceptions. 

This study found that: “When the outcomes were actually experienced, losses did not have as big an emotional impact as predicted. These authors suggested that the purported asymmetrical impact of losses vs. gains was a property of affective forecasts and not of actual experiences.” In other words, the researchers seemed to argue that what initially presented as Loss Aversion had more to do with emotional responses, which did not always align with losses vs gains. 

Other studies have found that Loss Aversion does not seem to arise in repetitive situations or single events with low stakes — indeed the latter phenomenon has been called ‘magnitude dependent Loss Aversion’. Another theory is that Loss Aversion is actually resultant from losses gaining your attention more effectively, so is less about the loss and more about the arousal or focus it initiates. This has been reframed as Loss Attention.  

In Practice

‘Stickk’ to a commitment. 

No, that is not a spelling error, this example is about Stickk the web site created by a Professor of Behavioural Economics at Yale University that uses Loss Aversion to help you stick to your behavioural commitments.

Stickk is an online service where you make a public commitment to do something and, if you fail to do it, you will make a donation to a cause or group that you detest. This has proved to be more motivating than rewarding yourself with donating to a group you admire.

Save more tomorrow pension. 

In the book Nudge, Sunstein and Thaler describe the story of the ‘save more tomorrow pension.’ This initiative was designed to encourage young people to invest in pensions using Loss Aversion. The pension plan cost nothing until the individual received a pay rise, and then that pay rise would automatically be directed into their pension fund. 

The resultant user experience was that the individual did not see a reduction in their disposable income, there was no loss, there was only less gain. This resulted in a 200% increase in contributions amongst the target group.  

Pensions part two — matching payments. 

In another initiative linked to pensions, some initiatives have experimented with ‘matching contributions’, so if an individual pays x, the employer would match it. Leveraging Loss Aversion involves paying that match upfront and then taking it away if the individual does not make their payment.  

‘No one ever got fired for hiring IBM’.

Have you heard of that familiar business saying? It refers to the fact that if you’re a manager and you hire the predictable ‘safe’ consultants to do work, you will not get fired, even if they don’t deliver results. However, if you go with the innovative, unknown group that might deliver more gains — the risk perception is higher and your job is more at risk as a result. It's essentially a tale in Loss Aversion

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Origins & Resources

Loss Aversion was first identified by psychologists Amos Tversky and Daniel Kahneman as part of their foundational work behind behavioural economics. The term was coined in 1979 but was more popularised in 1992 when Tversky and Kahneman started to actually measure the asymmetrical nature behind losses versus gain as part of their seminal work on Prospect Theory. 

Thinking, Fast and Slow is a mental model that I often reference at ModelThinkers, and it was originally a book that you should definitely dive into for more on Loss Aversion and associated heuristics.  

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