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WOLFRAM|DEMONSTRATIONS PROJECT

A Spatial Dynamic Jury Model

juror initial error proclivity
defendant is actually
guilty
innocent
jury size
12
acquittal ceiling
0
conviction floor
12
distance effect
1
stubbornness
0.33
convict threshold
0.6
ballot visualized
draggable
draggable
not draggable
new juror positions
juror positions
jury ballots
In this spatial and dynamic model of jury behavior, jurors initially vote to convict or acquit the defendant based on a random variable drawn from a distribution affected by: (1) the defendant's actual guilt or innocence, and (2) the location of each juror in a two-dimensional conceptual space that represents the juror's ability to accurately perceive the situation as well as his or her predilection towards conviction. On subsequent ballots (the second stage), the jurors revise their votes based on the weighted votes of the other jurors. The weights for each juror are determined by a distance measure between that juror and the other jurors and a "stubbornness factor" that gives a special weight to the juror's own prior vote. The balloting continues until a fixed point is reached or until ten ballots have been taken, at which point the jury is assumed to have reached exhaustion.
You can control both the initial location of the jurors within the space and various parameters that affect the dynamics of voting. The jurors displayed in the top graphic are locators that you can drag, provided the "draggable" control is set appropriately. Moving the jurors about alters the weights they place on the votes of their fellow jurors and may altersometimes quite dramaticallythe evolution of balloting. Alternatively, you can reset the entire jury pool to random new positions by moving the "new juror positions" control. You can also potentially affect the final result by changing the laws relating to jury size, the minimum number of guilty votes needed to convict (conviction floor), and the maximum number of guilty votes consistent with acquittal (acquittal ceiling). Finally, you can also alter the dynamics of "persuasion" by changing the effect of distance on voting weights (distance effect), the relative weight the juror places on his or her own prior vote (stubbornness), and the weighted fraction of votes needed to convict before a juror will vote to convict on the next iteration (convict threshold).
The top graphic of the output shows the positions and votes of the jurors. Black is guilty and orange is innocent. The bottom graphic of the output shows a chronology of the ballots taken by the jury. The control labeled "ballot visualized" provides an alternative visualization of jury dynamics by letting you select the ballot with which the top graphic is associated.
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