A model of learning from social and direct evidence:

The learner is tasked with learning the weight of a coin. She observes some number nL of flips. A second person, “Zach,” sees nZ flips and then bets on the outcome of the next flip. The learner doesn’t see the outcomes of Zach’s nZ flips, but she does see how he bets.

This is a reference implementation for https://doi.org/10.1037/dec0000088:

``````;;; Helper functions

(define (iota n start)
(define (helper n x)
(if (> x n)
'()
(pair x (helper n (+ x 1)))))
(helper n start))

(define (factorial n)
(apply * (iota n 1)))

(define (combo n k)
(/ (factorial n)
(* (factorial k) (factorial (- n k)))))

(define (binomial weight n)
(multinomial (iota n 0)
(map (lambda (x) (bin_prob weight n x))
(iota n 0) )))

(define (observe weight ndata obs)
(flip (bin_prob weight ndata obs)))

(define (bin_prob weight ndata obs)
(define n ndata)
(define k obs)
(define p weight)
(define nk (- n k))
(define np (- 1 p))
(* (combo n k) (expt p k) (expt np nk)))

(define (guess weight alpha)
(if (flip (* alpha weight))
1
0))

;;; Set data and priors

(define reliable-prior .75)

(define ss-nsamples 5)

(define zach-nsamples 10)

(define (act-prior)
(uniform-draw '(0 1)))

(define (weight-prior)
(uniform-draw '(.1 .3 .5 .7 .9)))

(define ss-obs 2)

;;; Zach's model

(define (zachs-choice z-obs ss-weight reliable)
(rejection-query

(define z-weight (weight-prior))
(define act (act-prior))
(define result (if reliable
(guess z-weight 100)
(guess z-weight 0)))

act

(and (observe z-weight zach-nsamples z-obs)
(equal? act result))))

;;; Learner's model

(define (ss-model)
(rejection-query
(define ss-weight (weight-prior))
(define zach-obs (binomial ss-weight zach-nsamples))
(define zach-reliable (flip reliable-prior))
(define zach (zachs-choice zach-obs ss-weight zach-reliable))

ss-weight

;; Assuming our observations and Zach's choice:
(and (observe ss-weight ss-nsamples ss-obs)
(equal? 1  zach))))

(hist (repeat 100 ss-model))
``````