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 linushof committed Jul 01, 2021 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 --- title: "Sampling Strategies in DfE - Pilot study" author: "Linus Hof" date: "2021" output: html_document --- # Study Description In this pilot study, choice data will be generated by applying the *comprehensive* and *piecewise sampling strategy* and hybrids thereof to a series of two-prospect gambles. The simulated data will be explored for characteristic patterns of (or differences between) sampling strategies under varying structures of the environment, i.e., the features of a gamble's prospects, and other aspects of the sampling and decision behavior (model parameters). # Dataset ## Agents Under each condition (sampling strategy x all possible parameter settings), all gambles are played by N = 150 synthetic agents. ## Gambles Two different types of two-prospect gambles will be tested: (a) Gambles, in which one of the prospects contains a safe outcome only and the other two risky outcomes (safe/risky gambles). (b) Gambles, in which both prospects contain of two risky outcomes (risky/risky gambles). All outcomes are in the gain range $\omega_i \geq 0$. {r} n_agents <- 150  # Model parameters **Switching probability:** $s$ is the positive (negative) probability increment added to (subtracted from) the unbiased attendance probability $p = .5$ with which agents draw the succesive single sample from the prospect they did not get their most recent single sample from. We vary $s$ between 0 to .4 in increments of .1. **Boundary type**: Can either be the minimum value *any* prospect's sum of random variable realizations must reach (absolute boundary) or the minimum value for the difference of these sums (relative boundary). **Boundary value:** **Noise parameter:** {r}