One of the cornerstones of behavioral economics, a descriptive model that attempts to explain what people will choose not what they should choose is the prospect theory.
Prospect theory has two big ideas: reference dependence and probability weighting. Important about the reference dependence is what it means, why it is important and what mechanisms created along with how it influences real-world decisions.
Traditional economic models assume that the value of money follows a pattern of diminishing marginal utility, that is the difference between $0 and $1000 seem subjectively much greater the difference between $100,000 in $101,000.
Marginal utility diminishes with each additional dollar you have. This concept makes intuitive sense and we all can imagine that a given amount of money is worth more to someone who has little than someone who has much, utility in traditional models depends on absolute wealth.
When faced with the difficult sort of decision almost everyone plays it safe. The answer lies in reference dependence. Prospect theory propose that people evaluate potential outcomes in terms of relative changes in wealth, not absolute wealth states.
When faced with a risky decision we think less about her bank account and more about weather will be better or worse off afterward. We all end up being more risk-averse in our economic decisions than we should be, usually because we want to avoid losing money.
Reference points are common and are evident everyday lives. Consumers love sales. 20% off regular prices from the perspective of our diminishing bank account means that we don’t save money when we buy something at 20% off but if we adopt the regular price as a reference point getting something on sale can seem psychologically like savings.
Marketers know this in use reference dependence to influence our purchasing decisions. This is why the concept of reference dependence it is one of the most central ideas of behavioral economics and will observe its influence in many many sorts of decisions.
A fundamental problem of information processing is how can one represent information with sufficient range to accommodate big numbers but with sufficient precision to resolve differences between small numbers. Your visual system solves this problem in a remarkable way: it adjust its point of reference based on the overall brightness.
When the world is dark the reference point decreases in tiny changes in absolute brightness increase a firing rate of neurons when the world is bright the reference point increases in much greater changes in absolute brightness are required to alter neurons firing.
These same principles apply to nearly every aspect of how your brain processes information, from her senses dear motor system and yes to your decision-making.
You maybe know how dopamine neurons in your brainstem respond to a range of rewards, specifically they change the rate of firing in a predictable manner they increase their firing rate when rewards are better-than-expected, but decrease their firing rate when rewards are worse than expected
And, if a reward is completely predictable as when there was advance warning that that reward would be delivered in the world the firing rate does not change at all. This was is the reward prediction error. The brain creates an expectation or reference point about what reward should be received at each point in time.
When rewards are better-than-expected or when an organism gains information about unexpected future rewards then dopamine neurons increase their firing rate, if rewards are worse than expected then dopamine neurons decrease the firing rate. Importantly some reward is exactly as expected the firing rates do not change at all.
Remember, dopamine neurons do not respond to rewards themselves they respond to whether reward was better or worse the current reference point. That’s the reward prediction error.