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| public:rem4:rem4-20:abstract [2020/01/27 17:13] – [Some Examples of Abstracts] thorisson | public:rem4:rem4-20:abstract [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
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| ====Structure of Abstracts==== | ====Structure of Abstracts==== |
| | //Each item below should take only 1-2 sentences in your abstract// | (unless otherwise noted) | | | //Each item below should take only 1-2 sentences in your abstract// | \\ (unless otherwise noted) | |
| | Outer context | A short introduction to the **field** or **context** of the research problem addressed. This places the research topic within the larger scientific context. Go for "the shortest introduction possible". | | | Outer context | A short introduction to the **field** or **context** of the research problem addressed. This places the research topic within the larger scientific context. Go for "the shortest introduction possible". | |
| | Research problem / question | A short introduction to the research problem. Explain the problem you are addressing, in as simple words as possible. Make sure you describe it at the right level of detail. Not too general - then you are repeating the above point; not too specific - then you leave out some context that is necessary for some of your readership. "The shortest introduction possible" (max 4 sentences). | | | \\ Research problem / question | A short introduction to the research problem. Explain the problem you are addressing, in as simple and few words as possible (but not fewer). Make sure you describe it at the right level of detail. Not too general - then you are repeating the above point; not too specific - then you leave out some context that is necessary for some of your readership. "The shortest problem presentation possible" (max 4 sentences). | |
| | Key Motivation | Why this work was worth doing. 1 sentence. Make sure the justification is scientific (not personal!). | | | Key Motivation | Why this work was/is worth doing. Make sure the justification is scientific (not personal! - stick to verifiable facts, in the context of scientific progress). | |
| | Prior attempts at addressing it | How has this issue been addressed before? | | | Prior attempts at addressing it | How has this issue been addressed before? | |
| | Why prior attempts are not enough | How do prior attempts fail at addressing the issue? | | | Why prior attempts are not enough | How do prior attempts fail at addressing the issue? | |
| | Results | What were your results? (max 4 sentences). | | | Results | What were your results? (max 4 sentences). | |
| | (Conclusion) | Sometimes done to emphasize the **main message** or conclusion of the work described in the paper. | | | (Conclusion) | Sometimes done to emphasize the **main message** or conclusion of the work described in the paper. | |
| | | If you succeed | in following this guideline you have a guaranteed **great, readable, informative** abstract that is neither longer nor shorter than it needs to be. In other words, the //perfect abstract.// | |
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| | \\ Good | Humans learn how to use language in a society of language users. No principles that might allow an artificial agents to learn language this way are known at present. We present work that addresses this challenge: Our auto-catalytic, endogenous, reflective architecture (AERA) supports the creation of agents that can learn natural language by observation. Our S1 agent learns human communication by observing two humans interacting in a realtime mock television interview, using gesture and situated language. Results show that S1 can learn multimodal complex language and multimodal communicative acts, using a vocabulary of 100 words with numerous free-form sentence formats, by observing unscripted interaction between the humans, with no grammar being provided to it a priori. The agent learns both the pragmatics, semantics, and syntax of complex sentences spoken by the human subjects on the topic of recycling of objects such as aluminum cans, glass bottles, plastic, and wood, as well as use of manual deictic reference and anaphora, all through observation. | \\ [[http://alumni.media.mit.edu/~kris/ftp/AAoNL-wHeadr.pdf|PDF]] | \\ Should mention explicitly what prior theories/approaches are missing, and whether the "auto-catalytic, endogenous, reflective" is a new approach. | | | \\ Good | Humans learn how to use language in a society of language users. No principles that might allow an artificial agents to learn language this way are known at present. We present work that addresses this challenge: Our auto-catalytic, endogenous, reflective architecture (AERA) supports the creation of agents that can learn natural language by observation. Our S1 agent learns human communication by observing two humans interacting in a realtime mock television interview, using gesture and situated language. Results show that S1 can learn multimodal complex language and multimodal communicative acts, using a vocabulary of 100 words with numerous free-form sentence formats, by observing unscripted interaction between the humans, with no grammar being provided to it a priori. The agent learns both the pragmatics, semantics, and syntax of complex sentences spoken by the human subjects on the topic of recycling of objects such as aluminum cans, glass bottles, plastic, and wood, as well as use of manual deictic reference and anaphora, all through observation. | \\ [[http://alumni.media.mit.edu/~kris/ftp/AAoNL-wHeadr.pdf|PDF]] | \\ Should mention explicitly what prior theories/approaches are missing, and whether the "auto-catalytic, endogenous, reflective" is a new approach. | |
| | \\ Decent | This paper presents a critique of expected utility theory as a descriptive model of decision making under risk, and develops an alternative model, called prospect theory. Choices among risky prospects exhibit several pervasive effects that are inconsistent with the basic tenets of utility theory. In particular, people underweight outcomes that are merely probable in comparison with outcomes that are obtained with certainty. This tendency, called the certainty effect, contributes to risk aversion in choices involving sure gains and to risk seeking in choices involving sure losses. In addition, people generally discard components that are shared by all prospects under consideration. This tendency, called the isolation effect, leads to inconsistent preferences when the same choice is presented in different forms. An alternative theory of choice is developed, in which value is assigned to gains and losses rather than to final assets and in which probabilities are replaced by decision weights. The value function is normally concave for gains, commonly convex for losses, and is generally steeper for losses than for gains. Decision weights are generally lower than the corresponding probabilities, except in the range of low probabilities. Overweighting of low probabilities may contribute to the attractiveness of both insurance and gambling. | \\ [[https://www.uzh.ch/cmsssl/suz/dam/jcr:00000000-64a0-5b1c-0000-00003b7ec704/10.05-kahneman-tversky-79.pdf|PDF]] | \\ Somewhat unclear whether the "value function" refers to the theory or measurements in the physical world (data). \\ Unclear who is doing the "overweighting" - people in general, prior theories, or this particular theory. | | | \\ Decent | This paper presents a critique of expected utility theory as a descriptive model of decision making under risk, and develops an alternative model, called prospect theory. Choices among risky prospects exhibit several pervasive effects that are inconsistent with the basic tenets of utility theory. In particular, people underweight outcomes that are merely probable in comparison with outcomes that are obtained with certainty. This tendency, called the certainty effect, contributes to risk aversion in choices involving sure gains and to risk seeking in choices involving sure losses. In addition, people generally discard components that are shared by all prospects under consideration. This tendency, called the isolation effect, leads to inconsistent preferences when the same choice is presented in different forms. An alternative theory of choice is developed, in which value is assigned to gains and losses rather than to final assets and in which probabilities are replaced by decision weights. The value function is normally concave for gains, commonly convex for losses, and is generally steeper for losses than for gains. Decision weights are generally lower than the corresponding probabilities, except in the range of low probabilities. Overweighting of low probabilities may contribute to the attractiveness of both insurance and gambling. | \\ [[https://www.uzh.ch/cmsssl/suz/dam/jcr:00000000-64a0-5b1c-0000-00003b7ec704/10.05-kahneman-tversky-79.pdf|PDF]] | \\ Somewhat unclear whether the "value function" refers to the theory or measurements in the physical world (data). \\ Unclear who is doing the "overweighting" - people in general, prior theories, or this particular theory. | |
| | \\ Bad | Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed. \\ In agent-based modeling (ABM), a system is modeled as a collection of autonomous decision-making entities called agents. Each agent individually assesses its situation and makes decisions on the basis of a set of rules. Agents may execute various behaviors appropriate for the system they represent—for example, producing, consuming, or selling. Repetitive competitive interactions between agents are a feature of agent-based modeling, which relies on the power of computers to explore dynamics out of the reach of pure mathematical methods (1, 2). At the simplest level, an agent-based model consists of a system of agents and the relationships between them. Even a simple agent-based model can exhibit complex behavior patterns (3) and provide valuable information about the dynamics of the real-world system that it emulates. In addition, agents may be capable of evolving, allowing unanticipated behaviors to emerge. Sophisticated ABM sometimes incorporates neural networks, evolutionary algorithms, or other learning techniques to allow realistic learning and adaptation. \\ ABM is a mindset more than a technology. The ABM mindset consists of describing a system from the perspective of its constituent units. A number of researchers think that the alternative to ABM is traditional differential equation modeling; this is wrong, as a set of differential equations, each describing the dynamics of one of the system's constituent units, is an agent-based model. A synonym of ABM would be microscopic modeling, and an alternative would be macroscopic modeling. As the ABM mindset is starting to enjoy significant popularity, it is a good time to redefine why it is useful and when ABM should be used. These are the questions this paper addresses, first by reviewing and classifying the benefits of ABM and then by providing a variety of examples in which the benefits will be clearly described. What the reader will be able to take home is a clear view of when and how to use ABM. One of the reasons underlying ABM's popularity is its ease of implementation: indeed, once one has heard about ABM, it is easy to program an agent-based model. Because the technique is easy to use, one may wrongly think the concepts are easy to master. But although ABM is technically simple, it is also conceptually deep. This unusual combination often leads to improper use of ABM. | [[https://www.pnas.org/content/99/suppl_3/7280|Link]] | \\ Way too long. Three paragraphs for an abstract is outrageous and unceccesary. \\ A tutorial occupies 2/3 of the abstract - it does not belong there at all. \\ Discrepancy of at least one term between the abstract and the paper's title. | | | \\ Bad | Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed. \\ In agent-based modeling (ABM), a system is modeled as a collection of autonomous decision-making entities called agents. Each agent individually assesses its situation and makes decisions on the basis of a set of rules. Agents may execute various behaviors appropriate for the system they represent—for example, producing, consuming, or selling. Repetitive competitive interactions between agents are a feature of agent-based modeling, which relies on the power of computers to explore dynamics out of the reach of pure mathematical methods (1, 2). At the simplest level, an agent-based model consists of a system of agents and the relationships between them. Even a simple agent-based model can exhibit complex behavior patterns (3) and provide valuable information about the dynamics of the real-world system that it emulates. In addition, agents may be capable of evolving, allowing unanticipated behaviors to emerge. Sophisticated ABM sometimes incorporates neural networks, evolutionary algorithms, or other learning techniques to allow realistic learning and adaptation. \\ ABM is a mindset more than a technology. The ABM mindset consists of describing a system from the perspective of its constituent units. A number of researchers think that the alternative to ABM is traditional differential equation modeling; this is wrong, as a set of differential equations, each describing the dynamics of one of the system's constituent units, is an agent-based model. A synonym of ABM would be microscopic modeling, and an alternative would be macroscopic modeling. As the ABM mindset is starting to enjoy significant popularity, it is a good time to redefine why it is useful and when ABM should be used. These are the questions this paper addresses, first by reviewing and classifying the benefits of ABM and then by providing a variety of examples in which the benefits will be clearly described. What the reader will be able to take home is a clear view of when and how to use ABM. One of the reasons underlying ABM's popularity is its ease of implementation: indeed, once one has heard about ABM, it is easy to program an agent-based model. Because the technique is easy to use, one may wrongly think the concepts are easy to master. But although ABM is technically simple, it is also conceptually deep. This unusual combination often leads to improper use of ABM. | \\ [[https://www.pnas.org/content/99/suppl_3/7280|Link]] | \\ Way too long. Three paragraphs for an abstract is outrageous and unceccesary. \\ A tutorial occupies 2/3 of the abstract - it does not belong there at all. \\ Discrepancy of at least one term between the abstract and the paper's title. | |
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| ====Finding Related Work==== | ====Finding Related Work==== |
| | When have I searched enough? | That depends on how "green" you are in your field of study. The bottom line is: You can be sure you missed at least one paper that is highly relevant to your work. Ergo: Keep looking until the last minute. Just don't miss the deadline. | | | When have I searched enough? | That depends on how "green" you are in your field of study. The bottom line is: You can be sure you missed at least one paper that is highly relevant to your work. Ergo: Keep looking until the last minute. Just don't miss the deadline. | |
| | Cited work: Is there a maximum? | No. Most journals and conferences put no limitations on the number of references one can have in a paper. \\ If the paper calls for a lot of references then you should try to include them all. \\ Using the rule of proportions: It is strange to see more than 30% of the words in a paper devoted to references (typically it will be between 5% and 10%). | | | Cited work: Is there a maximum? | No. Most journals and conferences put no limitations on the number of references one can have in a paper. \\ If the paper calls for a lot of references then you should try to include them all. \\ Using the rule of proportions: It is strange to see more than 30% of the words in a paper devoted to references (typically it will be between 5% and 10%). | |
| | Cited work: Is there a minimum? | Yes: >1. \\ Work with no references will not get published. \\ Exceptions include: Letters of Opinion; Presidential addresses; published dialogue; and perhaps a few other ones. | | | Cited work: Is there a minimum? | Yes: >1. \\ Work with no references will not get published. \\ Exceptions include: Letters of Opinion; Presidential addresses; published dialogue; and perhaps a few other ones. | |
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