Forecasting Argumentation Frameworks

Celem artykułu jest zdefiniowanie pewnego typu argumentacji, rzadko opisywanego do tej pory zarówno w polskiej, jak i zagranicznej literaturze z zakresu teorii argumentacji, a przez to usystematyzowanie problematyki badań nad zabiegami semantycznymi w argumentacji. Analiza, jak modyfikacja znaczenia może wpływać na moc argumentacji skłania do wyróżnienia nowego typu argumentacji – argumentacji semantycznej. Przeanalizowane zostaną też pojęcia definicji perswazyjnej i klasyfikacji oraz sposoby ich aplikacji w formułowaniu argumentu. Zaproponowana tutaj definicja argumentacji semantycznej może rodzić pewne problemy, typowe przy wprowadzaniu nowego pojęcia, dlatego druga część pracy jest poświęcona kwestiom związanym z zaproponowanym kształtem definicji. Podanie definicji jest niezbędne do dalszych badań nad argumentacją semantyczną, jej typologią i oceną poprawności.

Based on construal level theory, this work investigates the interaction between message source (artificial agent vs. human agent) and temporal framing (proximal vs. distal) to identify effective persuasive messages in the context of health communication advertising. Results of two online experiments in two different health contexts reveal a match effect between an artificial (human) agent and proximal (distal) temporal frame for attitude toward the message, intention to engage in healthy behaviours, to spread word-of-mouth, and to seek information about a health issue. Additionally, findings demonstrate that processing fluency and attitude toward the messages serially mediate the interplay between message source and temporal framing. Findings reveal how to effectively align message elements for messages created by artificial and human agents with temporal framing in health communication advertising. Our findings offer both theoretical implications and practical implications for health communication advertisers.

This paper examines the effect of redundancy among multiple individual forecasts on a decision maker's assessment of the expected accuracy of a consensus forecast. In the paper, forecast redundancy is defined as the existence of positive correlation among the errors of individual forecasts. Statistical theory indicates that the accuracy of a consensus forecast is a function of two factors: (1) the accuracy of individual forecasts composing the consensus, measured by their forecast error variances, and (2) the correlation among errors of these forecasts. Positive correlation among forecast errors reduces the accuracy of the consensus forecast. In this paper, I address the issue of whether decision makers' expected accuracy judgments reflect the negative impact of forecast redundancy. In addition, tests examine whether expected accuracy judgments are more likely to reflect the effect of individual forecast accuracy than forecast redundancy. Decision makers face two types of expected accuracy judgments when

ABSTRACTSimple linear combinations of forecasts have consistently been found to be more accurate than individual forecasts. Several recent studies have found that combination forecasts derived by constrained or unconstrained multiple regression are more accurate than a simple average of individual forecasts. This study uses macroeconomic data to compare the accuracy of combination forecasts derived by a Bayesian methodology with the accuracy of composite forecasts derived by multiple regression. Using the forecasts of four macroeconomic variables from five well‐known econometric models, the study finds that the Bayesian combination procedure produces more accurate composite forecasts than does the regression combination procedure, based on a version of Theil's U2 statistic.

This study investigates the impact that the quality of reported earnings has on the accuracy of financial analysts earnings forecasts. Extant research indicates that earnings attributes are important considerations to users of accounting information. One such attribute is earnings quality; often measured as the magnitude of accruals that do not convert to cash in a timely manner, where a poor match of cash flows and accruals indicates low earnings quality. Such accruals could reduce the usefulness of financial reports. This study uses two measurements of forecast accuracy to assess the impact that earnings quality has on the forecast accuracy of financial analysts. Following prior research, one measurement considers the environment in which the analyst operates and compares their accuracy to that of their peers. The second compares the individual analyst forecast to the actual reported earnings. For both measurements of accuracy the results show that higher earnings quality is associated with improved forecast accuracy.

This chapter proposes a fuzzy approach to forecasting using a financial data set. The methodology used is multiple objective linear programming (MOLP). Selecting an individual forecast based on a single objective may not make the best use of available information for a variety of reasons. Combined forecasts may provide a better fit with respect to a single objective than any individual forecast. We incorporate soft constraints and preemptive additive weights into a mathematical programming approach to improve our forecasting accuracy. We compare the results of our approach with the preemptive MOLP approach. A financial example is used to illustrate the efficacy of the proposed forecasting methodology.

In November 2007, the Federal Open Market Committee (FOMC) announced a change in the way it communicates its view of the economic outlook: It increased the frequency of its forecasts from two to four times per year, and it increased the length of the forecasting horizon from two to three years. The FOMC does not release the individual members' forecasts or standard measures of consensus such as the mean or median. Rather, it continues to release the forecast information as a range of forecasts, both the full range between the high and the low and a central tendency that omits the extreme values. This paper uses individual forecaster data from the Survey of Professional Forecasters (SPF) to mimic the FOMC's method for creating their central tendency. The authors show that the midpoint of the central tendency of the SPF is a reliable measure of the consensus, suggesting that the FOMC reporting method is also a reliable measure of consensus. For the dates when both are available, the authors also compare the relative forecast accuracy of the FOMC and SPF consensus forecasts for output growth and inflation. Overall, the differences in forecast accuracy are too small to be statistically significant.

Decomposition ensemble forecasting of air passenger demand is of continuing interest to researchers. The most common method employed to produce ensemble forecasts for demand forecasting is a linear combination of individual single‐component forecasts. Nevertheless, it remains unclear how linear combinations with different weight assessments affect the accuracy of decomposition ensemble forecasting. Besides, to strengthen the overall accuracy of decomposition ensemble forecasting, it is intriguing to investigate whether significant synergy can be created by combining forecasts yielded by individual decomposition ensemble models using different weight schemes. This paper proposed a hybrid approach using several well‐known weighting schemes to measure the weights of individual single‐mode forecasts. The optimal combination selection from individual decomposition ensemble models was then used to construct combined models to strengthen the accuracy of decomposition ensemble forecasting. The empirical results indicated that linear combinations with optimal weights significantly outperformed those with other considered weighting schemes. Interestingly, the combined forecasts generated by the optimal combination selection from individual decomposition ensemble models significantly outperformed the best individual ensemble forecasts. It has been found that the accuracy obtained by decomposition ensemble forecasting with equal weights was only average, and the optimal combination subsets included few such models. The proposed hybrid approach can help industry practitioners make operational plans.

The accuracy of inflation forecasts has important implications for macroeconomic stability and real interest rates in economies with nominal rigidities. Erroneous forecasts destabilize output, undermine the conduct of monetary policy under inflation targeting and affect the cost of both short and long-term government borrowing. We propose a new method for forecasting inflation that combines individual forecasts using time-varying-coefficient estimation along with an alternative method based on neural nets. Its application to forecast data from the US and the euro area produces superior performance relative to the standard practice of using individual or linear combinations of individual forecasts, especially during periods marked by structural changes.

Macroeconomic Derivatives: An Initial Analysis of Market-Based Macro Forecasts, Uncertainty and Risk

Management Insights

The aim of our research is to understand and automate the mechanisms by which language can emerge among artificial, knowledge-based and rational agents. We want to design and implement agents that, upon encountering other agent(s) with which they do not share an agent communication language, are able to initiate creation of, and further are able to evolve and enrich, a mutually understandable communication language. Our research is supported by the principled methodology of designing rational, socially competent artificial agents based on Bayesian probability and decision theories, and on the research in linguistics and cognitive psychology that addresses the issues of function, mechanisms, development, and evolutionary history of natural languages. In our work, we express some of the key insights obtained in linguistics and cognitive science in formal terms of decision theory and game-theoretic. mechanism design. We propose that the evolution of an agent communication language can be accomplished by the mechanism of negotiation, developed in economics and game theory, and automated in recent work in artificial intelligence. Negotiation is suitable because it can be mapped to settings in which rational interacting agents could use communication for their mutual, yet selfish, benefits. The agents can make mutually beneficial agreements that will allow efficient communication, but they have a conflict of interest about which language constructs to use each would prefer a communication language that is easier and less costly to use from their own individual perspective.

This book provides a framework for thinking about foundational philosophical questions surrounding machine learning as an approach to artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning’s current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the active engagement of general psychological faculties—such as perception, memory, imagination, attention, and empathy—enables rational agents to extract abstract knowledge from sensory experience. This book explains a number of recent attempts to model roles attributed to these faculties in deep-neural-network–based artificial agents by appeal to the faculty psychology of philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke, David Hume, William James, and Sophie de Grouchy. It illustrates the utility of this interdisciplinary connection by showing how it can provide benefits to both philosophy and computer science: computer scientists can continue to mine the history of philosophy for ideas and aspirational targets to find the way to create more robust rational artificial agents, and philosophers can see how some of the historical empiricists’ most ambitious speculations can be realized in specific computational systems.

As increasing numbers of processors and agents pervade the human environment, societies comprising both humans and agents will emerge. Presently, it is unknown how a person might fair in such mixed societies. For the societies to operate effectively and efficiently, it is important for the humans and agents to recognize and understand each other's behavior. This paper provides an initial step in that understanding via two contributions: (1) we provide models, within a limited domain, for agents that behave like humans and (2) we present the results of simulated interactions between the human-like agents and a variety of purely rational agents. Our models for the behaviors of people are based on recent sociological research by Simpson and Willer [10] that explores humans' cooperative prosocial behavior, a conceivably non-rational process. Modeling human behaviors presents a means of exploring and understanding motivations, consequences, and resolutions to human-agent interactions. We aspire to exploit measured human behavior in order to observe its ramifications in an agent world, and to motivate development of human-agent societies. Our results show that, although there are pitfalls to which humans are vulnerable, there exist niches for human prosperity in a rational agent world.

Introducing automated vehicles into road traffic and robots into urban public environments puts artificial agents in space-sharing conflicts with humans. Humans anticipate, avoid, and smoothly resolve space-sharing conflicts by implicit and explicit communication enabled by uniquely human shared intentionality. Smooth coordination among artificial, partly automated, teleoperated, and human agents needs to be supported by coactive design optimizing observability, predictability, and directability to foster robustness of operation. Available humans should be employed by ‘wizard interfaces’ to support automation, for example, in recognizing intentions, in anticipating difficulties, and in predicting for foresighted coordination. Human capabilities are not just helpful for efficiency and robustness but necessary to cope with exceptional events and failures. For quickly removing blocks and resuming operation, available humans should be employed and authorized by measures of ‘shepherd raising’. Because non-human agents are at a disadvantage in public environments and potentially block operation for extended intervals if they are bullied or denied help, benevolence and cooperation are essential in hybrid societies. Cooperation in public environments is higher in functional societies and can be fostered towards artificial agents in particular by coactive design increasing trust, by influencing mindless and mindful anthropomorphism, and further determinants of reciprocal altruism.