|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”.|
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/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?|
|Why prior attempts are not enough||How do prior attempts fail at addressing the issue?|
|Method||How did you do perform this work? (max 4 sentences at most).|
|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.|
|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 ber. In other words, the perfect abstract.|
|Keep it simple!||No one ever faulted a scientific paper for being too easy to read or having too simple wording or sentence structure, if all that matters is already in there.|
|Keep it concise||The abstract is the most polished part, wording wise, of scientific papers.|
|As simple as possible but not simpler||Make sure it's easy to read but says everything.|
|GRADE||EXAMPLE||REF||TO BE IMPROVED|
|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.|| ||
Should mention explicitly what prior theories/approaches are missing, and whether the “auto-catalytic, endogenous, reflective” is a new approach.
|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.|| ||
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.
| 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.
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.
|Abstract||This section is key - it's a mini-summary of your paper, intended to allow others to decide whether your work is relevant to their work (and whether they should read on)|
|Introduction||Overall context of the work, short summary of related work and a presentation of the motivation for the work - the problems that are to be addressed. Last paragraph: Explain the structure of the paper.|
|Motivation||Explicit presentation of the motivation (or fold this in with the Introduction, if the motivation can be expressed in 1-2 sentences).|
|Related work / Literature review||Relatively dry discussion of prior work and how it is inadequate in addressing the problems that your idea addresses. What have others done? Why is it not adequate for answering your own question(s)?|
|Contributions||Your idea, your work. This is the topic of the paper. Describe it as clearly as you can.|
|Evaluation||How do you make sure your idea is a good one? How do you convince others that it's a good idea?|
|Results||Present the results so that they support the claims made throughout - and support the idea that your idea (the topic of the paper) is worth publication.|
|Discussion||Optional section - sometimes things that did not fit into the paper but may be of some interest and relevance.|
|Conclusion||This is the conclusion you draw from the work, as presented in the paper. Based on what has been said in this paper, what conclusions can you draw? This is often a semi-summary of the paper.|
|References||A structured list of publications that relate to the work described in the paper.|
|Ask before you start your research|| This will determine your research context, experimental paradigm and the emphasis or slant you choose for your work.
This is especially important if you are working in interdisciplinary research or on projects that can appeal to more than one scientific community.
|Ask before you start writing your paper|| Select the journal / conference first.
Do a background search on papers recently published there, to verify that your background section and description of work fits into their context (less important for journals).
|Ask again when you do your background research||It is good to remind oneself every now and then about who one wants to read the paper. A very good time to ask this question is right before starting to do background research - online search for related material.|
|Pick your style - be consistent !|
|The fewer words the better||As few words as possible, but not fewer (to paraphrase Einstein).|
|Pointed paragraphs|| Make sure that each paragraph has a point. The last sentence should give the reason why the paragraph is there by tying into the work that the paper describes.
Example: “This work [reviewed in this paragraph] therefore shows that no solution has been found to the problem of X.” – where the paper is about finding a solution to X, or where X is related to the topic of the paper and is addressed as part of the paper.
|Structure: Prior work achievements and shortcomings||The main purpose of this section is to tie your work firmly to what has been done before. Therefore, the section has to show that there are shortcomings of prior work that need to be mended.|
|Support your main argument|| Remember: A scientific paper is an argument. The section on related work needs to support the main arguments made in the paper:
— Be selective on what papers you present in the section.
— Construct a narrative (tell a story), to keep the reader interested. Nobody likes to read a long, dry recount of what has been done. Use your motivation(s) (what questions are you trying to answer?) to keep the story interesting.
|Use topic to steer inclusion of related work||The major topic of your paper will tell you what you need to review. Use your title and abstract to figure out what work to review.|
|What is it?||A handy method to help you write a nice Related Work section|
|Step 1||Group the paper you have identified as related work into groups, where each group represents (a) a particular way of solving the problem at hand and (b) all the solution have particular shortcomings.|
|Step 2||(C) Write 2-3 sentences about what the researchers in the first group did; (d) write 1-2 sentences about the shortcomings of the work in this roup, wrt your own work (that is, write the shortcomings in a way that the reader sees why your own contribution is a direct response to these shortcomings|
|Step 3||Go back to Step 1. Repeat as often as needed (a reasonably-sized Related Works section contains at least 3 groups of related work papers).|
|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 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.
|Name-Year system|| Name of author and year listed; alphabetical in reference section.
Jones, J. P. (2002). Bass Playing Through the Ages. International Musician, 12(8): 232-234.
Pullman, J. (1999). The Effects of Toasters on Human Health. J. of Toasterology, 11(12): 11-22.
|Citation-sequence system|| Publications are numbered in the order they are cited.
 Pullman, J. (1999). The Effects of Toasters on Human Health. J. of Toasterology, 11(12): 11-22.
 Jones, J. P. (2002). Bass Playing Through the Ages. International Musician, 12(8): 232-234.
|Kyed|| [PULL99] Pullman, J. (1999). The Effects of Toasters on Human Health. J. of Toasterology, 11(12): 11-22.
[JON02] Jones, J. P. (2002). Bass Playing Through the Ages. International Musician, 12(8): 232-234.
|APA Style (Amer. Psychological Assoc.)|| Very common – possibly the most common reference style; used in many fields. The one we will use.
Book: Molich, Rolf (2003). Brugervenligt webdesign. Copenhagen: Teknisk Forlag.
Journal: Thórisson, K. R., H. Benko, A. Arnold, D. Abramov, S. Maskey, A. Vaseekaran (2004). Constructionist Design Methodology for Interactive Intelligences. A.I. Magazine, 25(4): 77-90. [OPTIONAL:] Menlo Park, CA: American Association for Artificial Intelligence.
Conference: Melson, G. F., Kahn, Jr., Peter H., Beck, A. M., Friedman, B., Roberts, T. and Garrett, E. (2005). Robots as Dogs? Children's Interactions with the Robotic Dog AIBO and a Live Australian Shepherd. Proceedings of CHI 2005, Philadelphia, PA, April 2-7, 33-39.
|Other styles||see e.g.: http://dal.ca.libguides.com/content.php?pid=860&sid=11818|