Political Implication of Presidential System Practice in The Jokowi-JK Government

This study focuses on the political implications of the presidential system practice in the Jokowi-JK government. Constitutionally, the Indonesian government system adheres to a presidential system. However, there is a system confusion in practice that causes the President's role and position as head of government to be not optimal because his policies are often criticized and even intervened by the DPR. The purpose of this study is to analyze the political implications of the presidential system practice run by Jokowi-JK, using a case study approach and analyzing primary and secondary data. The specification of the research is analytical descriptive with qualitative method. The theories used include presidential theory, party system, coalition, and leadership theory. The study results show that the presidential-parliamentary-style system run by Jokowi-JK has implications for less than optimal decision-making by the President in the preparation of the cabinet and disruption of relations with parliament.

Shortfall Risk Models When Information on Loss Function Is Incomplete

Utility-based shortfall risk measures effectively captures a decision maker's risk attitude on tail losses. In this paper, we consider a situation where the decision maker's risk attitude toward tail losses is ambiguous and introduce a robust version of shortfall risk, which mitigates the risk arising from such ambiguity. Specifically, we use some available partial information or subjective judgement to construct a set of plausible utility-based shortfall risk measures and define a so-called preference robust shortfall risk as through the worst risk that can be measured in this (ambiguity) set. We then apply the robust shortfall risk paradigm to optimal decision-making problems and demonstrate how the latter can be reformulated as tractable convex programs when the underlying exogenous uncertainty is discretely distributed.

Data-Driven Performance Evaluation Framework for Multi-Modal Public Transport Systems

Transport agencies require accurate and updated information about public transport systems for the optimal decision-making processes regarding design and operation. In addition to assessing topology and service components, users’ behaviors must be considered. To this end, a data-driven performance evaluation based on passengers’ actual routes is key. Automatic fare collection platforms provide meaningful smart card data (SCD), but these are incomplete when gathered by entry-only systems. To obtain origin–destination (OD) matrices, we must manage complete journeys. In this paper, we use an adapted trip chaining method to reconstruct incomplete multi-modal journeys by finding spatial similarities between the outbound and inbound routes of the same user. From this dataset, we develop a performance evaluation framework that provides novel metrics and visualization utilities. First, we generate a space-time characterization of the overall operation of transport networks. Second, we supply enhanced OD matrices showing mobility patterns between zones and average traversed distances, travel times, and operation speeds, which model the real efficacy of the public transport system. We applied this framework to the Comunidad de Madrid (Spain), using 4 months’ worth of real SCD, showing its potential to generate meaningful information about the performance of multi-modal public transport systems.

Subjective optimality in finite sequential decision-making

Many decisions in life are sequential and constrained by a time window. Although mathematically derived optimal solutions exist, it has been reported that humans often deviate from making optimal choices. Here, we used a secretary problem, a classic example of finite sequential decision-making, and investigated the mechanisms underlying individuals’ suboptimal choices. Across three independent experiments, we found that a dynamic programming model comprising subjective value function explains individuals’ deviations from optimality and predicts the choice behaviors under fewer and more opportunities. We further identified that pupil dilation reflected the levels of decision difficulty and subsequent choices to accept or reject the stimulus at each opportunity. The value sensitivity, a model-based estimate that characterizes each individual’s subjective valuation, correlated with the extent to which individuals’ physiological responses tracked stimuli information. Our results provide model-based and physiological evidence for subjective valuation in finite sequential decision-making, rediscovering human suboptimality in subjectively optimal decision-making processes.

Participation Improves Collective Decisions (When It Involves Deliberation): Experimental Evidence From Kenya

Citizen participation in decision making has been widely lauded as a method for improving societal outcomes. Deliberative discussion, in particular, is believed to be more transformative than a mere aggregation of individual preferences, leading to more socially optimal decision making and behavior. I report the results from a laboratory experiment with 570 subjects in Nairobi, directly testing the effect of participation in deliberative group decision making on collective outcomes. Participants engage in a group task to earn compensation toward a shared group fund. Randomly assigned treatments vary according to whether decision making over the task to be completed involves: (1) external assignment; (2) non-deliberative majority voting; or (3) consensus through deliberative discussion. I find that deliberation improves collective decision making. Deliberation is also associated with changes in preferences, greater agreement with decision outcomes, and greater perceived fairness. Evidence for behavior change is weaker, but there is some support for further research into the relationship between preference change and behavior change.

Master Data Governance Best Practices

Master data governance is not a new field, and it has existed since the early 90s (8). However, the demand for master data governance has been rising recently due to the ever-increasing demand for cost optimization, faster product innovations, compliance with the set rules and regulations, and competitive advantage in the business field. One of the areas in which companies can achieve master data governance is through maintaining consistent data quality. Handling data is one of the pain areas in most companies due to the complicities involved in managing data. Still, companies that succeed in handling data compete well in their business since data leads to optimal decision-making. Data mismatch is putting so many companies projecting to accelerate their growth on brakes. This paper outlines the best practices of master data governance, which can help the companies solve and improve their odds in data governance and improve their business quickly, predictably when planning and implementing the decisions made.

Uncertainty and Investment Choice in a Real-Options Model of the Firm

<p>A firm must consider many factors when adopting an investment policy including, but not limited to the size, scope, and cost of each investment, as well as the firm's financial condition. The multitude of considerations makes optimal decision-making much more complex than is indicated by standard real-option models of investment. This thesis investigates the behaviour of a cash-constrained firm that has access to two distinct investment opportunities. Such a firm must not only choose the timing of each investment, but often it must also choose between investments. When compared with similar one-project models of the past, the introduction of an additional investment opportunity alters the general results in a variety of ways. If one of the projects has a high yield, and therefore a quick payback period, this project can provide benefits over and above its NPV as the cash it generates relaxes future capital constraints for follow-up investment. When the firm is sufficiently constrained, this can lead to an investment policy where high-yield low-NPV projects are implemented instead of lower-yield higher-NPV projects, a direct deviation from the NPV rule. If one of the projects can raise a relatively large proportion of its value as collateral for investment, then the constrained firm will at times accelerate investment in this project in order to free up cash reserves for the other opportunity. In single-project models, when the firm is able to invest in a low NPV project, the value of additional cash is low. This is because the project will be delayed regardless of the level of cash. However, when the firm has a second investment opportunity, if one project has a low NPV and the other a high NPV then additional cash is beneficial to the firm. The two-project model also provides insights into how resources should be allocated if the constrained firm decides to split and operate the projects as separate firms. When cash is low, more resources should go to the spin-off with the high NPV project in order to give it the best chance of being initiated. However, when cash is high, disproportionately more resources should go to the spin-off with the lower NPV project as investment in the higher NPV project is likely to occur without the help of additional resources.</p>

Uncertainty and Investment Choice in a Real-Options Model of the Firm

<p>A firm must consider many factors when adopting an investment policy including, but not limited to the size, scope, and cost of each investment, as well as the firm's financial condition. The multitude of considerations makes optimal decision-making much more complex than is indicated by standard real-option models of investment. This thesis investigates the behaviour of a cash-constrained firm that has access to two distinct investment opportunities. Such a firm must not only choose the timing of each investment, but often it must also choose between investments. When compared with similar one-project models of the past, the introduction of an additional investment opportunity alters the general results in a variety of ways. If one of the projects has a high yield, and therefore a quick payback period, this project can provide benefits over and above its NPV as the cash it generates relaxes future capital constraints for follow-up investment. When the firm is sufficiently constrained, this can lead to an investment policy where high-yield low-NPV projects are implemented instead of lower-yield higher-NPV projects, a direct deviation from the NPV rule. If one of the projects can raise a relatively large proportion of its value as collateral for investment, then the constrained firm will at times accelerate investment in this project in order to free up cash reserves for the other opportunity. In single-project models, when the firm is able to invest in a low NPV project, the value of additional cash is low. This is because the project will be delayed regardless of the level of cash. However, when the firm has a second investment opportunity, if one project has a low NPV and the other a high NPV then additional cash is beneficial to the firm. The two-project model also provides insights into how resources should be allocated if the constrained firm decides to split and operate the projects as separate firms. When cash is low, more resources should go to the spin-off with the high NPV project in order to give it the best chance of being initiated. However, when cash is high, disproportionately more resources should go to the spin-off with the lower NPV project as investment in the higher NPV project is likely to occur without the help of additional resources.</p>