Review of Dominic Johnson’s God is Watching You

This paper builds on the theory of The Rhetorical Presidency to examine how rhetoric has served as a vehicle for presidents to use their approval ratings and bipartisanship to win support from Congress. It contains a case study of the State of the Union Addresses of five presidents from 1960 to 2010 and looks specifically at their rhetoric on foreign affairs. Overall, although the findings support the literature that presidents can prime their approval ratings, they also suggest that the volume of rhetoric is not a key determinant of the success of such efforts. Additionally, the findings support the literature that bipartisan rhetoric is ineffective in promoting bipartisanship in the roll call votes by Congress and further suggests that it is equally ineffective in influencing other stages of the legislative process.

China’s growing shipbuilding prowess is very relevant to the analysis of China’s defense and dual-use economies. A recent article from the Economic Daily Times says China’s shipbuilding development should focus on seeking deeper integration between civil and military shipbuilding to “develop areas of mutual benefit and raise ship technology to new levels.” Dual-use aspects of commercial shipbuilding are fewer than in the aviation and spaceflight sectors, for example, and direct civil-to-military technology transfer is limited. That said, the development of maritime industry human capital, indigenous industrial innovation, and the ability to rapidly build merchant-type ships all have important defense implications.

INTRODUCTION In 2005, estimates from state officials and tribal leaders suggested that 4,500 Native Americans lost everything in southeastern Louisiana during Hurricanes Katrina and Rita. Was there mass media coverage of the effect of these hurricanes on Native Americans in Louisiana? If so, then where was it? If not, then why wasn’t there any? What can be learned from a post-Katrina and -Rita examination of the media coverage about the inabilities of mass media to illuminate the impact the hurricanes had on Native Americans in Louisiana? Although it seems to have been a common journalistic belief that because the majority populations in many Louisiana cities covered by the media reports were either predominantly African American or Caucasian—therefore, small Native American populations will have their concerns addressed in the coverage of these groups—the problem with this formulation is that it may lend the illusion to the American public that African Americans and Caucasians were the only populations affected. The significance of this issue becomes clearer when one tries to understand why the Houma and Pointe-au-Chien nations—like many others—raised serious questions about the limited media coverage they received shortly after the hurricanes struck and about the coverage they still do not receive as they continue to struggle to return their lives to normal. The standard discourse in the social scientific literature surrounding this problem suggests that few people were aware that Native Americans still lived in Louisiana; many are not federally recognized nations but are state-recognized nations and therefore not “real” Indians, or their appearances make it hard to distinguish them from the larger African American and Caucasian populations. This lack of attention on the Native Americans is consistent with the waning of nationwide media attention on Katrina and Rita victims.

How the brain uses reinforcement feedback to make simple choices that lead to reward is well understood. However, this ability is often considered insufficient to account for the flexibility and efficiency of human decision-making. In this chapter, we show that the computations of model-free reinforcement learning (RL) can in fact account for complex human learning abilities, such as generalization, transfer, and fast learning in high-dimensional, dynamic environments. Specifically, we show that humans structure their current information and choices into useful state and action spaces and that applying simple RL computations to these spaces-sometimes hierarchically-enables rich decision-making. Thus, RL computations enable humans to learn to represent the information they acquire in structured ways. Such structured RL simplifies complex problems (through representation learning), affords transfer of information (by building abstract rules and relating them to relevant contexts), and enables efficient exploration (by grouping together subsequences or identifying subpolicies).