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Abstract
In this paper, Landemore explores one defense of political equality: that uncertainty about the nature of the political questions a polity will face require not only maximal inclusion but also egalitarian inclusion. While experts have an advantage in cataloging and quantifying known risks, there is no expertise in enumerating outcomes under uncertainty, when the very number and probability of possible outcomes are unknown. Democratic equality is thus "ecologically" rational, because best suited to this radical uncertainty.

This article argues that clicktivism is a legitimate political act. It emphasizes that such acts, through a recurrent negative discourse, have been marginalized. As a result, new modes of participation that draw upon the simplification of social connectivity have largely been ignored in the mainstream Political Science literature. In addressing this issue, the article develops a heuristic that identifies seven dimensions (or characteristics) of clicktivism. This new heuristic will allow for the analysis of clicktivism as a form of civic engagement separate from digital activism more broadly. Subsequently, the article applies the heuristic to several popular forms of clicktivism, including social buttons and the creation of memes, demonstrating both its utility and flexibility.

The paper discusses the urgent modernization necessity of both modern educational process and the English language teaching at the technological university, the importance and efficiency of creativity and heuristic approach being highlighted. Some scenario episodes of a specially designed innovative heuristic performance illustrate the results of its introduction, the dominant role of the English language in personal and professional students' competence formation being stated.

ABSTRACTThis article presents some of the basic concepts and definitions of the technique of heuristic programming. An analysis and criticism of the heuristic programming methodology is made. A survey of the existing literature demonstrates the power and the generality possible of heuristic programming. Furthermore, a look forward reveals likely future developments of teh technique.For several years now, impressive and extensive applications of heuristic programming have been made of various decision situations. Yet, seminars and discussions of heuristic programming continue to find the heuristic programming experts fielding basic questions such as the following: Where did heuristic programming originate? Does heuristic programming have a lexicon? What are some examples of heuristics? What is the heuristic approach? Hasn't the validity of heuristic programming been questioned? What progress has been made in using heuristic programming? What basic approaches have evolved in using heuristic programming? Is heuristic programming compatible with other research now being carried out? And what is the future of heuristic programming?This article brings the answers to these questions together for easy reference for researchers in the decision sciences of all areas.

What is heuristic inquiry, anyway? -- Locating heuristic inquiry within contemporary qualitative research -- Philosophical and theoretical foundations of heuristic inquiry -- Heuristic processes and phases -- Heuristic research design -- Heuristic data collection, organization, and analysis -- Relationality, reflexivity, and meaning-making -- Evaluating the research: a collaborative process -- Writing a living manuscript: an embodied relational approach -- Ethics of heuristic research -- Universal applications of heuristic inquiry: bridging research and living experience -- An ending, beginning

Introduction -- Part One: Principles. Chapter 1 -- Chapter 2. Contingency -- Chapter 3. Probability -- Chapter 4. Temporality -- Chapter 5. Strategic construction -- Chapter 6 'Heuremes' – Patterns of persuasive argument -- Part Two: Practice. Chapter 7. Classical Oratory -- Chapter 8. Modern Oratory.-Epilogue: Heuristic Rhetoric in Modern Education and Society.

Shortest path problems occupy an important position in operations research as well as in artificial intelligence. In this paper we study shortest path algorithms that exploit heuristic estimates. The well‐known algorithms are put into one framework. Besides, we present an interesting application of binary numbers in the shortest path theory.

Foreword : from method to transdisciplinary heuretics / Julie Thompson Klein -- 1. Introduction : sustainability, transdisciplinarity, and the complexity of knowing / Katri Huutoniemi -- 2. Thinking outward : heuristics for systemic understanding of environmental problems / Katri Huutoniemi and Risto Willamo -- 3. Ecosystem services in integrated sustainability assessment : a heuristic view / Jari Lyytimaki and Lars Kjerulf Petersen -- 4. Heuristics for framing sustainability problems in transport / David Banister -- 5. Exploring the space of alternatives : heuristics in sustainability scenarios / Petri Tapio, Mattias Hojer, Asa Svenfelt and Vilja Varho -- 6. From complexity to solvability : the praxeology of transdisciplinary research / Christian Pohl -- 7. Responding to communication challenges in transdisciplinary sustainability science / Troy E. Hall and Michael O'Rourke -- 8. Envisioning solutions : expert deliberation on environmental futures / Vilja Varho and Katri Huutoniemi -- 9. Understanding environmental heuristics : trust and dialogue / Riikka Paloniemi and Annukka Vainio -- 10. Heuristics as cognitive tools for pursuing sustainability / Janne Hukkinen and Katri Huutoniemi -- 11. Transdisciplinarity as sustainability / Robert Frodeman.

One of the best known ideas in the study of bounded rationality is Simon's satisficing; yet we still lack a standard formalization of the heuristic and its implications. We propose a mathematical model of satisficing which explicitly represents agents' aspirations and which explores both single-person and multi-player contexts. The model shows that satisficing has a signature performance-profile in both contexts: (1) it can induce optimal long-run behavior in one class of problems but not in the complementary class; and (2) in the latter, it generates behavior that is sensible but not optimal. The model also yields empirically testable predictions: in certain bandit-problems it pins down the limiting probabilities of each arm's use, and it provides an ordering of the arms' dynamical use-probabilities as well.