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Why did American leaders work hard to secure multilateral approval from the United Nations or NATO for military interventions in Haiti, Bosnia, and Kosovo, while making only limited efforts to gain such approval for the 2003 Iraq War? InReassuring the Reluctant Warriors, Stefano Recchia draws on declassified documents and about one hundred interviews with civilian and military leaders to illuminate little-known aspects of U.S. decision making in the run-up to those interventions. American leaders, he argues, seek UN or NATO approval to facilitate sustained military and financial burden sharing and ensure domestic support. However, the most assertive, hawkish, and influential civilian leaders in Washington tend to downplay the costs of intervention, and when confronted with hesitant international partners they often want to bypass multilateral bodies. In these circumstances, America's senior generals and admirals-as reluctant warriors who worry about Vietnam-style quagmires-can play an important restraining role, steering U.S. policy toward multilateralism. Senior military officers are well placed to debunk the civilian interventionists' optimistic assumptions regarding the costs of war, thereby undermining broader governmental support for intervention. Recchia demonstrates that when the military expresses strong concerns about the stabilization burden, even hawkish civilian leaders can be expected to work hard to secure multilateral support through the UN or NATO-if only to reassure the reluctant warriors about long-term burden sharing. By contrast, when the military stays silent, as it did in the run-up to the 2003 Iraq War, the most hawkish civilians are empowered; consequently, the United States is more likely to bypass multilateral bodies and may end up shouldering a heavy stabilization burden largely by itself. Recchia's argument that the military has the ability to contribute not only to a more prudent but also to a more multilateralist U.S. intervention policy may be counterintuitive, but the evidence is compelling.

This chapter contains sections titled: Rescuing American Citizens Peacekeeping Missions The Promotion of Democracy Bibliography

For market players and policy officials, commodity price forecasts are crucial problems that are challenging to address due to the complexity of price time series. Given its strategic importance, corn crops are hardly an exception. The current paper evaluates the forecasting issue for China’s weekly wholesale price index for yellow corn from January 1, 2010 to January 10, 2020. We develop a Gaussian process regression model using cross validation and Bayesian optimizations over various kernels and basis functions that could effectively handle this sophisticated commodity price forecast problem. The model provides precise out-of-sample forecasts from January 4, 2019 to January 10, 2020, with a relative root mean square error, root mean square error, and mean absolute error of 1.245%, 1.605, and 0.936, respectively. The models developed here might be used by market players for market evaluations and decision-making as well as by policymakers for policy creation and execution.

PurposeWhy do managers redesign global supply chains in a particular manner when faced with compounding geopolitical disruptions? In answering this research question, this study identifies a constrained system of reasoning (decision-making logic) employed by managers when they redesign their supply chains in situations of heightened uncertainty.Design/methodology/approachThe authors conducted 40 elite interviews with senior supply chain executives in 28 companies across nine industries from November 2019 to June 2020, when the UK was preparing to leave the European Union, the US–China trade war was escalating, and Covid-19 was spreading rapidly around the globe.FindingsWhen redesigning global supply chains, the authors find that managerial decision-making logic is constrained by three distinct environmental ecosystem conditions: (1) the perceived intensity of institutional pressures; (2) the relative mobility of suppliers and supply chain assets; and (3) the perceived severity of the potential disruption risk. Intense government pressure and persistent geopolitical risk tend to impact firms in the same industry, resulting in similar approaches to decision-making regarding supply chain design. However, where suppliers are relatively immobile and supply chain assets are relatively fixed, a dominant logic is consistently present.Originality/valueBuilding on an institutional logics perspective, this study finds that managerial decision-making under heightened uncertainty is not solely guided by institutional pressures but also by perceptions of the severity of risk related to potential supply chain disruption and the immobility of supply chain assets. These findings support the theoretical development of a novel construct that the authors term ‘supply chain logics’. Finally, this study provides a decision-making framework for Senior Executives competing in an increasingly complex and unstable business environment.

Private online platforms have an increasingly essential role in free speech and participation in democratic culture. But while it might appear that any internet user can publish freely and instantly online, many platforms actively curate the content posted by their users. How and why these platforms operate to moderate speech is largely opaque. This Article provides the first analysis of what these platforms are actually doing to moderate online speech under a regulatory and First Amendment framework. Drawing from original interviews, archived materials, and internal documents, this Article describes how three major online platforms - Facebook, Twitter, and YouTube - moderate content and situates their moderation systems into a broader discussion of online governance and the evolution of free expression values in the private sphere. It reveals that private content-moderation systems curate user content with an eye to American free speech norms, corporate responsibility, and the economic necessity of creating an environment that reflects the expectations of their users. In order to accomplish this, platforms have developed a detailed system rooted in the American legal system with regularly revised rules, trained human decision-making, and reliance on a system of external influence. This Article argues that to best understand online speech, we must abandon traditional doctrinal and regulatory analogies and understand these private content platforms as systems of governance. These platforms are now responsible for shaping and allowing participation in our new digital and democratic culture, yet they have little direct accountability to their users. Future intervention, if any, must take into account how and why these platforms regulate online speech in order to strike a balance between preserving the democratizing forces of the internet and protecting the generative power of our New Governors.

The emergence of SARS-CoV-2, the virus that causes COVID-19, has led to a global pandemic. The United States has been severely affected, accounting for the most COVID-19 cases and deaths worldwide. Without a coordinated national public health plan informed by surveillance with actionable metrics, the United States has been ineffective at preventing and mitigating the escalating COVID-19 pandemic. Existing surveillance has incomplete ascertainment and is limited by the use of standard surveillance metrics. Although many COVID-19 data sources track infection rates, informing prevention requires capturing the relevant dynamics of the pandemic. The aim of this study is to develop dynamic metrics for public health surveillance that can inform worldwide COVID-19 prevention efforts. Advanced surveillance techniques are essential to inform public health decision making and to identify where and when corrective action is required to prevent outbreaks. Using a longitudinal trend analysis study design, we extracted COVID-19 data from global public health registries. We used an empirical difference equation to measure daily case numbers for our use case in 50 US states and the District of Colombia as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. Examination of the United States and state data demonstrated that most US states are experiencing outbreaks as measured by these new metrics of speed, acceleration, jerk, and persistence. Larger US states have high COVID-19 caseloads as a function of population size, density, and deficits in adherence to public health guidelines early in the epidemic, and other states have alarming rates of speed, acceleration, jerk, and 7-day persistence in novel infections. North and South Dakota have had the highest rates of COVID-19 transmission combined with positive acceleration, jerk, and 7-day persistence. Wisconsin and Illinois also have alarming indicators and already lead the nation in daily new COVID-19 infections. As the United States enters its third wave of COVID-19, all 50 states and the District of Colombia have positive rates of speed between 7.58 (Hawaii) and 175.01 (North Dakota), and persistence, ranging from 4.44 (Vermont) to 195.35 (North Dakota) new infections per 100,000 people. Standard surveillance techniques such as daily and cumulative infections and deaths are helpful but only provide a static view of what has already occurred in the pandemic and are less helpful in prevention. Public health policy that is informed by dynamic surveillance can shift the country from reacting to COVID-19 transmissions to being proactive and taking corrective action when indicators of speed, acceleration, jerk, and persistence remain positive week over week. Implicit within our dynamic surveillance is an early warning system that indicates when there is problematic growth in COVID-19 transmissions as well as signals when growth will become explosive without action. A public health approach that focuses on prevention can prevent major outbreaks in addition to endorsing effective public health policies. Moreover, subnational analyses on the dynamics of the pandemic allow us to zero in on where transmissions are increasing, meaning corrective action can be applied with precision in problematic areas. Dynamic public health surveillance can inform specific geographies where quarantines are necessary while preserving the economy in other US areas.

While Prospect Theory helps to explain decision-making under risk, studies often base frames on hypothetical events and fail to acknowledge that many individuals lack the ability and motivation to engage in complex thinking. We use an original survey of US adults ( N  = 2813) to test Prospect Theory in the context of the May 2023 debt ceiling negotiations in the US Congress and assess whether objective numeracy moderates framing effects. We hypothesize and find evidence to suggest that most respondents are risk-averse to potential gains and risk-accepting to potential losses; however, high numerates are more risk-averse and risk-accepting to gains and losses, respectively, than low numerates. We also find that need for cognition interacts with numeracy to moderate framing effects for prospective losses, such that higher need for cognition attenuates risk-acceptance among low numerates and exacerbates risk-acceptance among high numerates. Our results are robust to a range of other covariates and in models accounting for the interaction between political knowledge and need for cognition, indicating joint moderating effects from two knowledge domains similarly conditioned by the desire to engage in effortful thinking. Our findings demonstrate that those who can understand and use objective information may remain subjectively persuaded by certain policy frames.

This study quantifies ship-type performance indicators by training intelligent agents to evaluate and score vessels. The Analytic Hierarchy Process (AHP) is then applied to assess the internal consistency of the collected data, ensuring its authenticity and validity. Subsequently, the entropy weight method is employed to objectively determine the significance of each indicator in ship-type decision-making. Finally, COSCO (China COSCO Shipping Corporation Limited) Shipping’s capacity gap reflects the results of the methodology: the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) technique ranks all feasible ship-type combinations, presenting their relative merits through quantitative results. A standardized grading system is further proposed to evaluate these combinations systematically. Ultimately, the 10 most suitable solutions are identified—none achieving the theoretical maximum rating of Grade 10—demonstrating room for improvement in vessel performance.

The tourism industry has been seriously suffering from the coronavirus disease (COVID-19) crisis ever since its outbreak. Given this pandemic situation, the major aim of this study is to develop a conceptual framework that clearly explains the US international tourists’ post-pandemic travel behaviors by expanding the theory of planned behavior (TPB). By utilizing a quantitative process, the TPB was successfully broadened by incorporating the travelers’ perceived knowledge of COVID-19, and it has been deepened by integrating the psychological risk. Our theoretical framework sufficiently accounted for the US tourists’ post-pandemic travel intentions for safer international destinations. In addition, the perceived knowledge of COVID-19 contributed to boosting the prediction power for the intentions. The associations among the subjective norm, the attitude, and the intentions are under the significant influence of the tourists’ psychological risks regarding international traveling. The comparative criticality of the subjective norm is found. Overall, the findings of this study considerably enhanced our understanding of US overseas tourists’ post-pandemic travel decision-making processes and behaviors.

Economic policy uncertainty (EPU) will affect the external business environment of economic entities, which in turn affects the decision-making of economic entities. Meanwhile, carbon emissions are closely related to the production decisions of microeconomic entities. Thus, studying the relationship between EPU and carbon emissions helps to clarify the impact of institutional factors behind carbon emissions, which is significant for achieving green development. Based on US sector data, we apply a novel parametric test of Granger causality in quantiles to analyze the relationship between EPU and carbon emissions (its growth and uncertainty). We find that there is an outstanding pattern of Granger-causality from the US EPU to the growth of carbon emissions in the tails of the growth distributions of carbon emissions in the industrial sector, residential sector, electric power sector, and transportation sector, except in the commercial sector. That is, carbon emissions are affected by EPU when the growth of carbon emissions is in a higher or lower growth period. Lastly, we find that the US EPU affects carbon emissions uncertainty over the entire conditional distribution for all sectors.