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I have made several blog posts on the code I use to compute rank dependent utility In this blogpost however I want to illustrate (by means of an example in R) how the choice of forward vs backward induction can impact the valuation of the same prospect.

### The Problem

To start lets direct our attention to our picture which shows a decision tree that shows the choice between two possible prospects. The upper branch from our square decision node has some uncertainty and our lower branch has no uncertainty at all. In the case of forward induction we reduce our probabilities and preserve our outcomes values but in the case of backward induction we compute the certainty equivalents up to the first stage of uncertainty. (This simple example is based on Appendix C in Peter Wakker’s Prospect Theory for Risk and Ambiguity).

Under expected utility, the valuation of the upper branch of this decision tree will be the same regardless of if we use forward or backward induction. In Rank dependent utility theory however We have very different valuations. Lets Illustrate this with the RDU_Data() function in the code below. (you can just copy and paste this in your R terminal for the results).

#RDU Algorithm Code RDU_Data<-function(outcomes,p_vec,pw){ if(length(outcomes)==length(p_vec)){ #Step 0 organize outcomes and probabilities into a single vector df<-data.frame(outcomes,p_vec) #Step 1 Organize Probabilities by outcome. df1<-df[order(-outcomes),] zerorow<-c(0,0) df2<-rbind(zerorow,df1) #Step 2 Define vector of ranks rank<-cumsum(df2[,2]) df3<-data.frame(df2,rank) #Step 3 compute pweights pw_vec<-pw(df3$rank) df4<-data.frame(df3,pw_vec) #Step 4 Take difference between pw_vec to compute decision weights d_weights<-diff(df4$pw_vec) #Check if weights sum to 1 sum(d_weights) #Add to dataframe d_weights1<-c(0,d_weights) df5<-data.frame(df4,d_weights1) #Step 5 drop the first "helper" row df6<-df5[-1,] colnames(df6)<-c("Outcomes","Probabilities","Rank", "Weighted Ranks", "Decision Weights") return(df6)} else{ print("Outcomes and vector of probabilities must be the same length") } } #Prospect 1- Forward Induction payoffs_1<-c(100,0,49) probs_1<-c(0.56,0.14,0.3) #Prospect 2- Backward induction payoffs_2<-c(80,49) probs_2<-c(0.7,0.3) #Prob weighting function pwf<-function(p){p^2} #Run RDU_Data for each case RDU_1<-RDU_Data(payoffs_1,probs_1,pwf) RDU_2<-RDU_Data(payoffs_2,probs_2,pwf) #Valuations finduct_payoff_EU<-payoffs_1%*%probs_1 binduct_payoff_EU<-payoffs_2%*%probs_2 finduct_payoff_RU<-payoffs_1%*%RDU_1$`Decision Weights` binduct_payoff_RU<-payoffs_2%*%RDU_2$`Decision Weights` #Table data.frame(finduct_payoff_EU, binduct_payoff_EU, finduct_payoff_RU, binduct_payoff_RU)

The output from all this code gives us a table with both the payoffs of each method under Expected Utility and Rank Dependent Utility as follows:

finduct_payoff_EU binduct_payoff_EU finduct_payoff_RU binduct_payoff_RU 1 70.7 70.7 44.1196 64.19

we see that the expected utility for each case is the same but our forward induction valuation and backward induction valuation differ.

### Why is there a difference in valuations

To see why there is a difference in valuations we need to remember that the rank dependent algorithm uses cumulative probabilities as ranks, weights them and calculates the difference between these weighted ranks to get our decision weights. When we use either forward or backward induction we are transforming the way in which ranks are calculated and and in turn the decision weights used to compute rank dependent utility.

This is illustrated from the output of `RDU_1`

and `RDU_2`

below.

# RDU_1 Output Outcomes Probabilities Rank Weighted Ranks Decision Weights 11 100 0.56 0.56 0.3136 0.3136 3 49 0.30 0.86 0.7396 0.4260 2 0 0.14 1.00 1.0000 0.2604 # RDU_2 Output Outcomes Probabilities Rank Weighted Ranks Decision Weights 2 80 0.7 0.7 0.49 0.49 3 49 0.3 1.0 1.00 0.51

### So do we compute RDU with Forward or Backward Induction?

The question of using either forward or backward induction would appear to be an empirical question of how decision makers approach prospects as modelled by a decision tree. There is some guidance provided by Rakesh Sarin and Peter Wakker in their 1998 paper “Dynamic Choice and Non Expected Utility” which invoke a condition of “sequential consistency” which assumes decision makers to commit themselves to either using forward or backward induction (or more broadly a “Family of models”). Its a tough paper to read, but my impression is that much like everything in life, you are forced to make a choice on how to compute your model.

### Conclusion

Rank dependent utility theory is famous for correcting issues with the original version of prospect theory 1979 (namely violations of first order stochastic dominance) and helped with creating a new and improved version of cumulative prospect theory in 1992. But it appears that as one problem was solved a new problem was opened up- in this case its the difference in valuations of forward vs backward induction.

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**Continue reading**: Issues with Rank Dependent Utility: Valuation Differences in Forward VS Backward Induction

## The Implication of Forward vs Backward Induction on Rank Dependent Utility

The author of the text brings attention to a crucial phenomenon in Rank Dependent Utility theory through an example manipulated with R programming. The problem encountered involves a choice between two prospects with variant uncertainties and the differing impact of forward and backward induction.

### Understanding the Exemplary Problem

The exemplar utilizes a decision tree, where one branch possesses uncertainty while the other does not. In the case of forward induction, it is noted that probabilities are reduced yet keeps outcomes values intact. However, with backward induction, the initial stage of uncertainty computes certainty equivalents.

### Different Valuations in Rank Dependent Utility Theory

In expected utility, the valuation of both sides of a decision tree will be identical regardless of whether we apply forward or backward induction. Nevertheless, in Rank Dependent Utility theory, this situation varies drastically. The valuation difference arising from this example using the RDU_Data() function has profound implications.

An understanding of these differences becomes crucial with realizing the rank-dependent algorithm uses cumulative probabilities as ranks, weights them, and then calculates these weighted ranks differences to form our decision weights. When forward and backward induction is applied, transformations occur in rank calculation and subsequently in decision weights which are useful in computing rank-dependent utility.

## Possible Future Developments & Actionable Advice

The question arises: Should one compute Rank Dependent Utility with Forward or Backward Induction? The choice seems to be empirical and greatly dependent on how decision-makers approach prospects as modeled by a decision tree. Based on guidelines in Sarin and Wakker’s 1998 paper “Dynamic Choice and Non Expected Utility”, it is suggested that decision makers commit to either using forward or backward induction, or more generally a “Family of models”.

### What Lies Ahead?

The Rank Dependent Utility theory emerged due to its profound advantage in correcting issues with the original version of prospect theory in 1979, such as violations of first-order stochastic dominance. However, while solving a problem, it also unveiled another – the difference in valuations of forward vs backward induction.

### Actionable Advice

It is thus vital for decision makers to weigh the implications of each approach and decide accordingly. Given the difference in valuation results that can occur depending on the choice of forward or backward induction, this choice should be made carefully, taking into account the specific conditions and requirements of each situation. Stakeholders should also look towards further research to refine this process and mitigate the impact of such variations.