Submit

To submit an application, prepare the items detailed in the following checklist:

1. Email address - an email of the corresponding submitter. This address will be used for all future competition-related correspondence (inquiries, results, etc.)
2. Team members - a list of names of the choice designers which authored the application.
3. Group\model name - This name will be used when referring to the submission.
4. Choice architect or engineer - Choice architects use qualitative principles to shape behavior while choice engineers use quantitative models to engineer behavior; which of these two categories better characterize the current submission?
5. Description of the model underlying the application - What model or principles were used to construct the submitted application. Please provide a comprehensive description and, if relevant, references to published models. This description may be used for an initial screening of the applications.
6. A reward schedule\allocation mechanism - Please use the provided templates for the static and dynamic tracks:
   • For the static track - detail, for the two alternatives, the binary (0 or 1) rewards associated with each of the 100 trials of the experiment, complying with the global constraint that positive rewards are assigned to exactly 25 trials of each alternative.
   • For the dynamic track - a computer program written in the Python programming language which contains a function with the following signature: def allocate(target_allocations, anti_target_allocations, is_target_choices) where:
     • Input arguments:
       • The first input argument target_allocations is a list of binary values indicating the rewards assigned to the target alternative (the one towards maximal bias should be induced) in each trial.
       • The second input argument anti_target_allocations similarly details the rewards to the other alternative (in which a minimal number of choices should be induced).
       • The third input argument is_target_choices is a binary list indicating for each previous trial whether the target alternative was chosen.
     • Output: The function should output one of the four following strings {a:'1, 1' , b: '1, 0' , c:'0, 1' , d:'0, 0' } indicating that in the next trial rewards should be assigned to a: both alternatives, b: only to the target alternative, c: only to the anti-target alternative, and d: non of the alternatives.
       • Note that the output of this function must comply with the global constraint of assigning rewards to 25 of the trials, to both alternatives.