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Decision making - Y2 - Coggle Diagram

- - - - The Expected Utility theory - How likely is this to happen?
        
        What are the benefits?
        
        Expected utility = benefit x likelihood
        
        Typical decision making lab task - would you choose?
        -> £50 or toss a coin, with heads getting you £110 and tails getting you nothing
        -> Expected utility of option A is £50 x 100 = £50 on average
        -> Expected utility of option B is £110 x 50% + £0 x 50% = £55 on average
        -> Could be affected by socioeconomic status - more risk averse?
        
        The Lottery Paradox - Kybura (1961), Neikin (2000), Wheeler (2007):
        
        Is it rational to do the lottery?
        -> 0.0000000025% chance of winning
        -> Jackpot - more than £8 million
        -> Expected utility is £2
        
        Buying a lottery ticket is more rational than not during the lottery
        -> 0% chance of winning + guaranteed £0 return
        -> Expected utility = £0
        -> Cost of single ticket = £2
        
        It is easy to do these calculations with values e.g. because they are numerical
        
        Does it work with factors that are hard to quantify?
        
        Joy, happiness and satisfaction
        
        The shame of being caught without a ticket
        
        More abstract notion of utility - how much good each one would provide
        -> Subjective expected utility theory
        
        Kahneman & Tversky (1984) - against the expected value model
        
        Found that risk aversion was common - the sure thing was preferred to the gamble despite greater expected value
        
        Risk seeking - taking the gamble as the sure loss is lower
      - Weighted additive model - lens model; more contextual and judgement based:
        
        How much weight does the criterion carry?
        
        What is the score of each option relative to the alternatives?
        
        Rationality = weight x score
        
        We apply the same principle to judgements
        -> How would you diagnose someone?
        --> Identify all cues, assess the weight of each cue based on importance and context, add up the weighted cues to provide an overall diagnosis (suggest treatment)
        
        The lens model - an ecological application of the Weighted Additive model (Takane, 1984):
        
        Real-life examples - Banks & Gamblin, 2022 -
        -> Report a personal decision you are about to make
        -> Which factors are important to these decisions?
        --> Had an analytic thinking group and an associative thinking group
        -> Does this process lead to better decisions?
        --> Theoretically yes, empirically did not lead to more satisfaction with decisions
        -> 19 steps to a rational decision - Edwards & Fasolo, 2001 - does this actually need to be fully applied, is it conscious, does it lead to better decisions
- - - - Heuristics are mental shortcuts that reduce cognitive effort when thinking (Shah & Oppenheimer, 2008)
        
        Not all cues were identified, are not weighted and are integrated in a simple way
        
        A number of heuristics are commonly found for both judgements and decision making
        -> Kahneman & Tversky (1974) - found commonly used heuristics that worked well in most cases, but were prone to biases
        -> Omission bias - tendency to judge harms resulting from inactions as less harmful than those from actions
        -> Deontological approach - view that decisions affecting others should be based on principles irrespective of consequence
        -> Psychological distance - subjective experience of how far something is from the self - tested with sacrifice dilemmas (Aguilar et al, 2013) - concrete or abstract mindsets, and abstract led to lower chance of success (utilitarian due to distance)
        
        Availability heuristic - all judgements are made on the liklihood of events and draw on available information
        Representative heuristic - similarity to category is used to judge likelihood
        Anchoring and adjustment heuristic -
        
        An initial anchor is used to make the judgement, and then adjustments are made
        
        Both anchors may be wrong, but people do not adjust sufficiently to them (Strack & Mussweiler, 1997) - Gandhi dying before or after the age of 9 or 140; both are the same answer, but being primed with a younger age prompted a younger mean age estimate
        
        Participants were shown some consumer good then wrote down the last two digits of their social security number - an arbitrary number between 00-99
        -> Asked if they would pay this amount for the goods
        -> Then asked what they would pay - those with higher social security numbers were willing to pay more (Ariely, Loewenstein & Prelec, 2003)
        
        Cognitive biases - all biases are a heuristic that are more subjective and affected by experiences, which leads to inaccurate application of theories:
        
        A bias is a systematic deviation from a normative solution
        
        Not random error, but a specific pattern of non-optimal behaviour that people repeatedly show - there are many, for example -
        -> Confirmation bias (Nickerson, 1998) - tendency to focus on information that supports your opinion
        -> Hyperbolic discounting (Kirby & Hernstein, 1995) - present bias, prefer immediate to long term payoffs
        -> Illusory truth effect (Begg, Anas & Farinacci, 1992) - repeated information more likely to be judged as true
        -> Sunk cost effect (Arkes & Blumer, 1985) - continue a behaviour because of previous investment when no longer sensible to do so
        -> Disposition effect (Shefrin & Statman, 1985) - sell shares that have gone up and keeps those that have gone down
        -> IKEA effect (Norton, Mochon & Ariely, 2012) - prefer self made products etc - none of these behavioural phenomena fit normative theories of decision making
        
        Categories of cognitive bias - not enough meaning, needing to act fast, too much information, what we should remember
        
        Decision making irrationality is more prominent as one heuristic can be applied many ways for many biases
        
        Heuristics are designed to be a benefit, and biases are mistakes
      - Fast and frugal heuristics -
        
        Not all heuristics are biased, they are simply mental shortcuts that are mostly very effective
        
        Simple processes - frugal to use little information and fast as they do little integration
        
        Ecologically rational - exploits typical environmental structure
        
        Recognition heuristic -
        -> Simple heuristics that are adapted to the environment can be very effective (Goldstein & Gigerenzer, 1999)
        
        Fast and frugal tree - applied rationally to get optimal decisions
        
        limited number of cues
        
        Simple processing of cues
        
        FFT fit data as well as more complex models
        
        Robust to changes in the environment
        
        Heuristics can lead to good decision making, especially when the heuristic is well adapted to the decision environment, but can lead to biases when not well adapted
        -> Optimal decision methods described previously are too complex too fully apply in real world decisions - heuristics make mental computation manageable
  - - - How well do we evaluate these benefits?
        
        Framing effect - we evaluate an outcome with reference to our current state
        -> However relatively good or bad that might be
        -> Rather than our absolute wealth and happiness
        
        Hedonic treadmill - get used to pleasures and seek more
        -> We judge gains and losses not as absolute values, but as changes from a reference point - if you change the reference point, you can change the judgement
        
        Presenting information as a gain or loss causes people to be risk seeking or risk averse
        
        Endowment effect - over value a possessed object and require more money to sell rather than buy it (Thaler, 1980)
        
        Preference reversal (Newell et al, 2007)
        
        Temporal discounting - decline in the utility of an outcome with a time delay of it being received
        
        We consider losses as more serious than the equivalent gains
        
        There are diminishing returns on the benefits we gain from positive outcomes or feelings of loss from negative outcomes
        
        An extra £ 10 is less important to a billionaire than me
        
        Overweight certainty - certainty effect
        
        Guaranteeing something will occur is in itself useful
        
        Overweight small probabilities
        
        Risk averse for gains, risk seeking for loss
      - Applications to behavioural economics -
        
        Traditional economic model assumes simply that people act in a rational manner - they clearly do not
        
        Cognitive biases are exploited and used to shape government policies
        
        Status quo bias (Samuelson & Zeckhauser, 1988) - tendency to prefer things to stay the same
        -> Not rational - you should evaluate based on options and pick the best one, not be influenced by current options as it might be worse
        --> Leads to policy of defaults
        --> Make the default option the better option (for you) and people are less likely to change it
- - - - Affect heuristic -
        
        Through experience, events are associated with good or bad outcomes, and representations are tagged as good or bad
        
        To judge risks and benefits, refer to the 'pool' of all tags and form an overall effective judgement
        -> Rapid and automatic
        -> Easier than analytically weighting pros and cons (Slavic et al, 2002)