Effect of attribution on the emotions and behavioral intentions of third-party observers toward intergroup discriminators during the COVID-19 pandemic

The global outbreak of novel coronavirus disease COVID-19 has caused intergroup discrimination associated with the disease to become increasingly prominent. Research demonstrates that the attitudes and behaviors of third-party observers significantly impact the progression of discrimination incidents. This study tested a parallel mediating model in which the attribution tendencies of observers influence their behavioral intentions through the mediating effect of the emotions of anger and contempt. The first two studies confirmed the proposed model with discrimination incidents reported against “returnees from Wuhan” and “returning workers from Hubei.” Study 3 further manipulated the attribution tendencies of observers, providing empirical evidence for the causality from attribution tendencies to emotions, confirming the validity of the model. These findings enrich the cognitive (attribution)–emotion–action model, further enhancing our understanding of the role of third parties in intergroup conflicts, with implications for the management of people’s emotions and behaviors in social crises.

Tourists Interest in Camping as an Alternative Tourism Activity During COVID-19 Pandemic: The AIDA Model Application

Since the coronavirus pandemic hit Indonesia, tourists have become more careful when doing tourist activities. Natural or outdoor tourist destinations are assumed as safe and preferable destinations because they have the lowest risk of coronavirus transmission. One of the activities that can be done in natural or outdoor tourist destinations is camping. Using the AIDA (Attention, Interest, Desire, and Action) Model, this research was conducted to determine how much tourists are interested in camping, especially in South Bandung, West Java, Indonesia, as it has many natural tourist destinations with camping areas. This study uses a quantitative descriptive method, frequency analysis techniques, and scoring analysis. Samples were taken using a stratified random sampling technique to 111 respondents. Respondents are tourists who know or have visited one of the three natural tourist destinations in South Bandung, used as samples, namely Mount Puntang, Ranca Upas, and Rancabali. The results show that tourists are quite interested in camping as an alternative tourism activity during the coronavirus pandemic. However, they have some limitations or prerequisites in visiting the camping area during these uncertain times.

Chapter 2. Symbolic Reasoning in Latent Space: Classical Planning as an Example

Symbolic systems require hand-coded symbolic representation as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems. To address the gap between the two fields, one has to solve Symbol Grounding problem: The question of how a machine can generate symbols automatically. We discuss our recent work called Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We discuss several key ideas that made Latplan possible which would hopefully extend to many other symbolic paradigms outside classical planning.

Small Farmers Large Field (SFLF): a synchronized collective action model for improving the livelihood of small farmers in India

This study pilots the collective action model “Small Farmers Large Field (SFLF)” to overcome the disadvantages faced by millions of small and marginal farmers due to diseconomies of scale and lack of bargaining power in the supply chain. This model is participatory and flexible and allows small farmers to benefit from achieving economies of scale by organizing themselves into groups and synchronizing and harmonizing selected operations. It was piloted in two villages of Odisha, an eastern Indian state, with 112 farmers (35 females and 77 males). These farmers organized themselves into groups and synchronized their operations such as nursery bed management, transplanting, and harvesting collectively to achieve economies of scale. The SFLF farmers purchased inputs (seed and fertilizer) and sold paddy as a group to increase their bargaining power in price negotiations. The results from this pilot study showed that the participating farmers almost doubled their profits. Apart from the monetary benefits, these farmers saved time in many joint activities, including input (seed and fertilizer) purchase, paddy sale, and nursery bed management. Other important benefits of the harmonization and synchronization of farming operations were social harmony and sustainability of the farming system.

Diversity Interventions in the Classroom: From Resistance to Action

The diversity intervention-resistance to action model is presented along with interviews of biology faculty undertaken to understand how resistance to implementing diversity-enhancing classroom interventions manifests at four specific input points within a rational decision-making process that too often results in inaction.

Mass action model of solution activity via speciation by solvation and ion pairing equilibria

Solutes and their concentrations influence many natural and anthropogenic solution processes. Electrolyte and solution models are used to quantify and predict such behavior. Here we present a mechanistic solution model based on mass action equilibria. Solvation and ion pairing are used to model speciated solute and solvent concentrations such that they correlate to a solution’s vapor pressure (solvent activity) according to Raoult’s law from dilute conditions to saturation. This model introduces a hydration equilibrium constant (Kha) that is used with either an ion dissociation constant (Kid) or a hydration modifier (m) with an experimentally determined ion dissociation constant, as adjustable parameters to fit vapor–liquid equilibrium data. The modeled solvation equilibria are accompanied by molecular dynamics (MD) studies that support a decline in the observed degree of solvation with increased concentration. MD calculations indicate this finding is a combination of a solvent that solvates multiple solutes, and changes in a solute’s solvation sphere, with the dominant factor changing with concentration. This speciation-based solution model is lateral to established electrostatics-based electrolyte theories. With its basis in mass action, the model can directly relate experimental data to the modeled solute and solvent speciated concentrations and structures.

Global Climate Policy and Collective Action: A Comment

In a recent issue of Global Environmental Politics, Aklin and Mildenberger (2020) argue against the prevailing characterization of climate change cooperation as a problem of free riding or collective action. The authors argue that models of collective action imply (1) policy reciprocity and (2) inaction in the absence of formal agreements to limit free riding. They argue that neither empirical implication is supported by an review of states' climate policy to date. In this comment we note that standard collective action models imply neither of the above hypotheses. As a result the empirical tests advanced in the original article are uninformative as to the explanatory power of the collective action model for international climate politics.