072bc16c5dfc9324…

Preprint
ID: 5e2dd305099d1d001855a9c32,809 bytesFirst seen Jun 24, 20261 collection

Fields

NameValue
abstractWilliam Riker argued political skills and strategies can be developed through two methods: instruction or practice. Simulations offer opportunities for students to receive instruction in political strategies and practice developing political skills without the real-world consequences faced by policymakers. Budget simulations introduce students to collective action problems prevalent at all levels of American government. While developing and passing a budget provides students with practice, carefully constructing the simulation introduces the heresthetic tactics of agenda setting, strategic voting, and dimension manipulation. By incorporating these strategies into the simulation, students must navigate conflicting interests—maximize personal gains and risk not adopting a budget, or cooperate and approve a budget that may not align with personal preferences. When used in American government and political institution courses, the budget simulation helps students identify choices policymakers encounter and develop the skills necessary for navigating those situations and the broader policymaking process.
abstractViews899
approvedDate2020-01-27T14:50:44.872Z
assetFileNameSmith & Phillips TLC Paper.pdf
assetFileSizeBytes153197
assetMimeTypeapplication/pdf
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authorIds["name:alex smith","name:stephen phillips"]
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citations0
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contentDownloads430
contentTypeWorking Paper
contentTypeId5ce663395846762193c9c430
doi10.33774/apsa-2020-8f263
doiUrlhttps://doi.org/10.33774/apsa-2020-8f263
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keywords["TLC2020","Simulations and Games","Heresthetic","Budget","Collective Action"]
licenseId5cc9d9bf7d4e0000ac04ef27
publishedDate2020-01-27T14:50:44.887Z
statusPUBLISHED
statusDate2020-01-27T14:50:44.887Z
subjectId5e68cb1bd1f19d49ce3ac752
submittedDate2020-01-26T18:27:55.779Z
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title"Shut it Down!": Teaching Heresthetic by Simulating the Budget Process
version1
vornull
webLinks[]

Canonical JSON

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Appears in 1 collection

cambridge-up/open-engage1 version · Jun 24, 2026

Full hash:072bc16c5dfc93243ef74d259c3eafc169accc7adc6bc35ef7520165e3c5a2bf