Series Of Original Experiments Research Articles

Does President Trump's Outrageous Behavior Work?: Results from Two Randomized‐Controlled Trials

AbstractPoliticians are increasingly relying on outrage to engage the public. President Donald Trump used outrage to fuel his unlikely 2016 presidential victory and 2020 reelection effort. Was Trump's outrageous behavior a boon to his political fortunes or political malpractice? I draw on research in political psychology, populism, and presidential appeals to examine President Trump's outrageous behavior and its effect on the public. Using a series of original experiments, I find support for my expectation that Trump's outrageous behavior is a politically advantageous public relations strategy. My results show that Trump's outrageous behavior increases his support among self‐identified Independents. However, contrary to popular conception, the results do not support claims that racial resentment or affinity for populism makes individuals more amenable to Trump's outrageous behavior.

Hybrid Intelligence in Architectural Robotic Materialization (HI-ARM): Computational, Fabrication and Material Intelligence for Multi-Mode Robotic Production of Multi-Scale and Multi-Material Systems

With increasing advancements in information and manufacturing technologies, there is an ever‑growing need for innovative integration and application of computational design and robotic fabrication in architecture. Hybrid Intelligence in Architectural Robotic Materialization (HI-ARM) provides methods and frameworks that target this need. HI-ARM introduces methodologies and technologies that incorporate computational, fabrication and material intelligence in integrated design-to-robotic-production workflows. The intelligence is explored at multiple architectural scales (Macro, Meso, Micro) through hybridization of building processes or multi-mode robotic production and multi-materiality. Porosity, Hybridity, and Assembly are introduced as main constituents for materialization frameworks relying on computational design and robotic production. These are tested in a series of original experiments that are presented in this thesis together with four peer-reviewed published papers discussing the process of developing integrated design-to-production methodologies in detail. The contributions show how both architectural materialization processes and building products can be customized in different phases and scales. Moreover, the developed discourse and definitions address the impacts of this research through the lenses of computation and automation in research, education, and practice in the fields of Architecture, Engineering, and Construction.

Hybrid Intelligence in Architectural Robotic Materialization (HI-ARM)

With increasing advancements in information and manufacturing technologies, there is an ever‑growing need for innovative integration and application of computational design and robotic fabrication in architecture. Hybrid Intelligence in Architectural Robotic Materialization (HI-ARM) provides methods and frameworks that target this need. HI-ARM introduces methodologies and technologies that incorporate computational, fabrication and material intelligence in integrated design-to-robotic-production workflows. The intelligence is explored at multiple architectural scales (Macro, Meso, Micro) through hybridization of building processes or multi-mode robotic production and multi-materiality. Porosity, Hybridity, and Assembly are introduced as main constituents for materialization frameworks relying on computational design and robotic production. These are tested in a series of original experiments that are presented in this thesis together with four peer-reviewed published papers discussing the process of developing integrated design-to-production methodologies in detail. The contributions show how both architectural materialization processes and building products can be customized in different phases and scales. Moreover, the developed discourse and definitions address the impacts of this research through the lenses of computation and automation in research, education, and practice in the fields of Architecture, Engineering, and Construction.

Partisan Media Exposure and Attitudes Toward the Opposition

How has the rise of partisan media outlets changed how citizens perceive the other party? In particular, does watching partisan news sources make citizens dislike and distrust the other party? Drawing on social identity theory, I explain how the slanted presentation of the news on partisan outlets leads viewers to perceive the other party more negatively, to trust them less, and to be less supportive of bipartisanship. Using a series of original experiments, I find strong support for my arguments. I conclude by discussing the normative and empirical implications of these findings.[Supplementary material is available for this article. Go to the publisher's online edition of Political Communication for the following free supplemental resource(s): full details of the experiments, including descriptions of the samples, protocol, and text of the stimuli, results of manipulation checks, the replication of experiment 2 described in the text, and additional statistical results.]

Why Do Partisan Media Polarize Viewers?

The recent increase in partisan media has generated interest in whether such outlets polarize viewers. I draw on theories of motivated reasoning to explain why partisan media polarize viewers, why these programs affect some viewers much more strongly than others, and how long these effects endure. Using a series of original experiments, I find strong support for my theoretical expectations, including the argument that these effects can still be detected several days postexposure. My results demonstrate that partisan media polarize the electorate by taking relatively extreme citizens and making them even more extreme. Though only a narrow segment of the public watches partisan media programs, partisan media's effects extend much more broadly throughout the political arena.