Currently, the widely used notion of activity is increasingly present in computer science. However, because this notion is used in specific contexts, it becomes vague. Here, the notion of activity is scrutinized in various contexts and, accordingly, put in perspective. It is discussed through four scientific disciplines: computer science, biology, economics, and epistemology. The definition of activity usually used in simulation is extended to new qualitative and quantitative definitions. In computer science, biology and economics disciplines, the new simulation activity definition is first applied critically. Then, activity is discussed generally. In epistemology, activity is discussed, in a prospective way, as a possible framework in models of human beliefs and knowledge.
AkerkarSR. Analysis and Visualization of Time-varying Data using the Concept of ‘Activity modeling’. Master’s thesis, Department of Electrical and Computer Engineering, University of Arizona, 2004.
2.
TocherKD. The Art of Simulation. English University Press, 1963.
3.
BalciO. The implementation of four conceptual frameworks for simulation modeling in high-level languages. In WSC ’88: Proceedings of the 20th conference on Winter simulation. New York, NY: ACM Press, 1988, pp. 287–295.
4.
PiddM. Object-orientation and three phase simulation. In Proceedings of the Winter Simulation Conference, 1992, pp. 689–693.
5.
CatmullEE. A Subdivision Algorithm for Computer Display of Curved Surfaces. PhD thesis, Department of Computer Science, University of Utah, December1974.
6.
ClarkJ. Hierarchical geometric models for visible surface algorithms. Communications of the ACM1976; 19: 547–554.
7.
FunkhouserTASequinCH. Adaptive display algorithm for interactive frame rates during visualisation of complex virtual environments. Proceedings of SIGGRAPH’93, 1993, pp. 247–254.
8.
Francisco RamosJChoverM. Level of detail modelling in a computer game engine. In RauterbergM (ed.), Proceedings of ICEC ( Lecture Notes in Computer Science, vol. 3166). Berlin: Springer, 2004, pp. 451–454.
9.
HornP. Autonomic Computing: IBM’s Perspective on the State of Information Technology. Manifesto, IBM Research, October2001.
10.
SterrittRParasharMTianfieldHUnlandR. A concise introduction to autonomic computing. Advanced Engineering Informatics2005; 19: 181–187.
11.
WeiserM. Ubiquitous computing (abstract). In ACM Conference on Computer Science, 1994, p. 418.
12.
LelandWEOttTJ. Load balancing heuristics and process behavior. ACM Performance Evaluation Review1986; 14: 54–69.
13.
GiavittoJ-LGodinCMichelOPrusinkiewiczP. Computational models for integrative and developmental biology. Modelling and Simulation of Biological Processes in the Context of Genomics. Paris: Hermes, 2002.
14.
CoquillardPHillDRC. Modélisation et Simulation des Ecosystèmes. Paris: Masson, 1997.
15.
von NeumannJ. Theory of Self-reproducing Automata. University of Illinois Press, 1966.
16.
MuzyAHillDRC. What is new with the activity world view in modeling and simulation? using activity as a unifying guide for modeling and simulation. In Winter Simulation Conference, Phoeninx, AZ, 2011, pp. 2887–2899.
17.
Ivan-ChesnevarCMaguitmanAGLouiRP. Logical models of argument. ACM Computer Surveys2000; 32: 337–383.
18.
BuxtonJNLaskiJ. Control and simulation language. The Computer Journal1962; 5: 194–199.
19.
ZeiglerBPKimTGPraehoferH. Theory of Modeling and Simulation. New York: Academic Press, 2000
20.
ZeiglerBP. Theory of Modeling and Simulation. New York: John Wiley & Sons, 1976.
21.
MuzyAJammalamadakaRZeiglerBPNutaroJJ. The activity tracking paradigm in discrete-event modeling and simulation: The case of spatially continuous distributed systems. Simulation1962; 5: 449–464.
22.
MuzyAInnocentiEWainerGAielloASantucciJF. Specification of discrete-event models for fire spreading. Simulation2005; 81: 103–117.
23.
WainerGGiambiasiN. Application of the cell-DEVS paradigm for cell spaces modeling and simulation. Simulation2001; 76: 22–39.
24.
MuzyANutaroJJ. Algorithms for efficient implementation of the DEVS and DS-DEVS abstract simulators. In Open International Conference on Modeling and Simulation (OICMS’05), 2005, pp. 273–279.
25.
MuzyAInnocentiEAielloA. Dynamic structure cellular automata in a fire spreading application. In Proceedings of ICINCO 2004, vol. 3, 2004, pp. 143–151.
26.
SiregarPSinteffJPJulenNLe BeuxP. An interactive 3D anisotropic cellular automata model of the heart. Computers and Biomedical Research1998; 31: 323–347.
27.
GosperRW. Exploiting regularities in large cellular spaces. Physica D: Nonlinear Phenomena1984; 10: 75–80.
28.
ChenHHHuangTS. A survey of construction and manipulation of octrees. Computer Vision, Graphics, and Image Processing1988; 43: 409–431.
29.
TansPKeelingR. Trends in Atmospheric Carbon Dioxide: Recent Mauna Loa CO2. National Oceanic and Atmospheric Administration (NOAA), 2012. http://www.esrl.noaa.gov/gmd/ccgg/trends/
30.
CoquillardP. Simulation of the cyclical process of heathlands: Induction of mosaic structures, evolution to irreversible states. Ecological Modelling1995; 80: 97–111.
31.
MalinvaudE. Lectures on miroeconomic theory. Amsterdam: North Holland, 1972.
32.
BeckerGS. The Economic Approach to Human Behavior. University of Chicago Press, 1976.
33.
SimonHA. From substantive to procedural rationality. Method and Appraisal in Economics. Cambridge: Cambridge University Press, 1980, p. 129.
34.
AumannRJ. Rule Rationality vs.Act Rationality. The Hebrew University, Center for the Study of Rationality, 2008.
35.
LeonardDLongNV. Optimal Control Theory and Static Optimization in Economics. Cambridge: Cambridge University Press, 1992.
36.
GigerenzerGGoldsteinDG. Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review1996; 103: 650–669.
37.
KahnemanDSlovicPTverskyA. Judgment under uncertainty: Heuristics and Biases. Cambridge: Cambridge University Press, 2008.
38.
PollockJH. Procedural epistemology. The Digital Phoenix - How Computers are Changing Philosophy. London: Wiley-Blackwell, 1998, pp. 17–36.
39.
BonnefonJF. Le raisonneur et ses modèles. PUG, 2011.
40.
SimonHA. A behavioral model of rational choice. The Quarterly Journal of Economics1955; 69: 99–118.
41.
GoldmanA. Epistemology and Cognitio. Harvard: Harvard University Press, 1988.
42.
FodorJA. Modularity of Mind: An Essay on Faculty Psychology. Cambridge, MA: The MIT Press, 1983.
43.
FodorJA. The Mind Doesn’t Work That Way: The Scope and Limits of Computational Psychology. Cambridge, MA: The MIT Press, 2000.
44.
CarruthersP. The Architecture of the Mind. Oxford: Oxford University Press, 2006.
45.
BarrettHCKurzbanR. Modularity in cognition: Framing the debate. Psychological Review2002; 113: 628–647.
46.
PollockJH. Plans and decisions. Theory and Decision2005; 57: 79–107.
47.
MuzyAZeiglerBP. Introduction to the activity tracking paradigm in component-based simulation. The Open Cybernetics and Systemics Journal2008; 2: 48–56.
48.
TononiGMcIntoshARRussellDPEdelmanGM. Functional clustering: Identifying strongly interactive brain regions in neuroimaging data. Neuroimage1998; 7: 133–149.
49.
ThagardP. Computation and the philosophy of science. The Digital Phoenix - How Computers are Changing Philosophy. London: Wiley-Blackwell, 1998, pp. 48–61.
50.
ThagardP. Societies of minds: Science as distributed computing. Studies in History and Philosophy of Science1993; 24: 49–67.
51.
DennettD. Artificial life as philosophy. Artificial Life1995; 1: 291–292.
52.
GärdenforsP. Mind-reading as control theory. European Review2007; 15: 223–240.
53.
GärdenforsP. Conceptual Spaces: The Geometry of Thought. Cambridge, MA: The MIT Press, 2000.
54.
JeannerodM. Motor Cognition: What Actions Tell the Self. Oxford: Oxford University Press, 2006.
55.
ZeiglerBMuzyAYilmazL. Artificial intelligence in modeling and simulation. In Encyclopedia of Complexity and Systems Science. New York: Springer, 2009, pp. 344–368.
56.
BoudonR. The Art of Self-persuasion: The Social Explanation of False Beliefs (translated by SlaterMalcolm). Cambridge: Polity, 1994.
