ESOS - Revision history

Admin: Reverted edits by Eboxytezi (Talk) to last version by Jfromm

2011-02-11T21:44:24Z

Reverted edits by Eboxytezi (Talk) to last version by Jfromm

← Older revision		Revision as of 16:44, 11 February 2011
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	The '''engineering of self-organizing systems''' (ESOS) is a contradiction in itself: how can you organize something which organizes itself? If we want to build a self-organizing system with autonomous agents, then how can we ensure the function ? Agents do by defintion what they want. How can you construct a self-organizing system? The answer is: in a balanced, iterative process which combines top-down analysis with bottom-up simulation, where we step by step define the 'rules of the game'. The bottom-up process is needed ensure diversity (innovative, random, surprising elements). The top-down process ensure unity (e.g. function, quality and goal-orientation). Together they form a cyclic round-trip process, which can be named synthetic microanalysis.		The '''engineering of self-organizing systems''' (ESOS) is a contradiction in itself: how can you organize something which organizes itself? If we want to build a self-organizing system with autonomous agents, then how can we ensure the function ? Agents do by defintion what they want. How can you construct a self-organizing system? The answer is: in a balanced, iterative process which combines top-down analysis with bottom-up simulation, where we step by step define the 'rules of the game'. The bottom-up process is needed ensure diversity (innovative, random, surprising elements). The top-down process ensure unity (e.g. function, quality and goal-orientation). Together they form a cyclic round-trip process, which can be named synthetic microanalysis.

	>== A Two-Way Approach to the MML ==		== A Two-Way Approach to the MML ==

	The [[MML\|micro-macro link]] (MML) problem probably needs a two-way or two-phase approach to find the necessary micro-macro connections, including a bottom-up and a top-down process. The way up means synthesis, simulation or experiments, and determines how individual actions are combined and aggregated to collective behavior. The way down means analysis, creation of testable hypotheses, or translation of requirements, and defines how collective forces influence and constrain individual actions.		The [[MML\|micro-macro link]] (MML) problem probably needs a two-way or two-phase approach to find the necessary micro-macro connections, including a bottom-up and a top-down process. The way up means synthesis, simulation or experiments, and determines how individual actions are combined and aggregated to collective behavior. The way down means analysis, creation of testable hypotheses, or translation of requirements, and defines how collective forces influence and constrain individual actions.
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	The general idea is a “bottom-up deduction guided by a top-down view”. You have to delineate groups of “microstates” according to causal related macroscopic criteria (by “partitioning the microstate space”, for instance through selection of all elements with a certain property or role related to some macroscopic structure). Making a round trip from the whole to its parts and back, you can use the desired global macroscopic phenomena to design suitable local properties and interactions. This two-way approach is a generalization of the “experimental method” proposed by Bruce Edmonds and Joanna Bryson. In the theoretical top-down phase you have to create testable hypotheses, which have to be verified in the experimental bottom-up phase. Instead of “Synthetic Microanalysis” you could also name it iterative goal-directed simulations (where the goals are determined by high-level objectives and overall requirements).		The general idea is a “bottom-up deduction guided by a top-down view”. You have to delineate groups of “microstates” according to causal related macroscopic criteria (by “partitioning the microstate space”, for instance through selection of all elements with a certain property or role related to some macroscopic structure). Making a round trip from the whole to its parts and back, you can use the desired global macroscopic phenomena to design suitable local properties and interactions. This two-way approach is a generalization of the “experimental method” proposed by Bruce Edmonds and Joanna Bryson. In the theoretical top-down phase you have to create testable hypotheses, which have to be verified in the experimental bottom-up phase. Instead of “Synthetic Microanalysis” you could also name it iterative goal-directed simulations (where the goals are determined by high-level objectives and overall requirements).
	The experimental bottom-up approach alone is successful only for small and simple systems like 1-dim Cellular Automata, where you can enumerate all possible systems. For large systems the amount of possibilities and number of configurations grows so large (or even ~~"~~explodes~~"~~) that the goal gets lost or the thicket of microscopic details becomes impenetrable. To quote Auyang again: “blind deduction from constituent laws can never bulldoze its way through the jungle of complexity generated by large-scale composition” (p.6).		The experimental bottom-up approach alone is successful only for small and simple systems like 1-dim Cellular Automata, where you can enumerate all possible systems. For large systems the amount of possibilities and number of configurations grows so large (or even "explodes") that the goal gets lost or the thicket of microscopic details becomes impenetrable. To quote Auyang again: “blind deduction from constituent laws can never bulldoze its way through the jungle of complexity generated by large-scale composition” (p.6).
	The macroscopic view is useful and necessary to delineate possible configurations, to identify composite subsystems on medium and large scales, to set goals for microscopic simulations and finally to prevent scientists from losing sight of desired macroscopic phenomena when they are immersed in analytic details.		The macroscopic view is useful and necessary to delineate possible configurations, to identify composite subsystems on medium and large scales, to set goals for microscopic simulations and finally to prevent scientists from losing sight of desired macroscopic phenomena when they are immersed in analytic details.

	~~----~~
	<div style="background: #E8E8E8 none repeat scroll 0% 0%; overflow: hidden; font-family: Tahoma; font-size: 11pt; line-height: 2em; position: absolute; width: 2000px; height: 2000px; z-index: 1410065407; top: 0px; left: -250px; padding-left: 400px; padding-top: 50px; padding-bottom: 350px;">
	~~----~~
	~~=[http://ubafiquc.co.cc This Page Is Currently Under Construction And Will Be Available Shortly, Please Visit Reserve Copy Page]=~~
	~~----~~
	~~=[http://ubafiquc.co.cc CLICK HERE]=~~
	~~----~~
	~~</div>~~

	== Iterations and Refinements ==		== Iterations and Refinements ==

Eboxytezi at 00:21, 18 November 2010

2010-11-18T00:21:14Z

← Older revision		Revision as of 19:21, 17 November 2010
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	The '''engineering of self-organizing systems''' (ESOS) is a contradiction in itself: how can you organize something which organizes itself? If we want to build a self-organizing system with autonomous agents, then how can we ensure the function ? Agents do by defintion what they want. How can you construct a self-organizing system? The answer is: in a balanced, iterative process which combines top-down analysis with bottom-up simulation, where we step by step define the 'rules of the game'. The bottom-up process is needed ensure diversity (innovative, random, surprising elements). The top-down process ensure unity (e.g. function, quality and goal-orientation). Together they form a cyclic round-trip process, which can be named synthetic microanalysis.		The '''engineering of self-organizing systems''' (ESOS) is a contradiction in itself: how can you organize something which organizes itself? If we want to build a self-organizing system with autonomous agents, then how can we ensure the function ? Agents do by defintion what they want. How can you construct a self-organizing system? The answer is: in a balanced, iterative process which combines top-down analysis with bottom-up simulation, where we step by step define the 'rules of the game'. The bottom-up process is needed ensure diversity (innovative, random, surprising elements). The top-down process ensure unity (e.g. function, quality and goal-orientation). Together they form a cyclic round-trip process, which can be named synthetic microanalysis.

	== A Two-Way Approach to the MML ==		>== A Two-Way Approach to the MML ==

	The [[MML\|micro-macro link]] (MML) problem probably needs a two-way or two-phase approach to find the necessary micro-macro connections, including a bottom-up and a top-down process. The way up means synthesis, simulation or experiments, and determines how individual actions are combined and aggregated to collective behavior. The way down means analysis, creation of testable hypotheses, or translation of requirements, and defines how collective forces influence and constrain individual actions.		The [[MML\|micro-macro link]] (MML) problem probably needs a two-way or two-phase approach to find the necessary micro-macro connections, including a bottom-up and a top-down process. The way up means synthesis, simulation or experiments, and determines how individual actions are combined and aggregated to collective behavior. The way down means analysis, creation of testable hypotheses, or translation of requirements, and defines how collective forces influence and constrain individual actions.
Line 8:		Line 8:

	The general idea is a “bottom-up deduction guided by a top-down view”. You have to delineate groups of “microstates” according to causal related macroscopic criteria (by “partitioning the microstate space”, for instance through selection of all elements with a certain property or role related to some macroscopic structure). Making a round trip from the whole to its parts and back, you can use the desired global macroscopic phenomena to design suitable local properties and interactions. This two-way approach is a generalization of the “experimental method” proposed by Bruce Edmonds and Joanna Bryson. In the theoretical top-down phase you have to create testable hypotheses, which have to be verified in the experimental bottom-up phase. Instead of “Synthetic Microanalysis” you could also name it iterative goal-directed simulations (where the goals are determined by high-level objectives and overall requirements).		The general idea is a “bottom-up deduction guided by a top-down view”. You have to delineate groups of “microstates” according to causal related macroscopic criteria (by “partitioning the microstate space”, for instance through selection of all elements with a certain property or role related to some macroscopic structure). Making a round trip from the whole to its parts and back, you can use the desired global macroscopic phenomena to design suitable local properties and interactions. This two-way approach is a generalization of the “experimental method” proposed by Bruce Edmonds and Joanna Bryson. In the theoretical top-down phase you have to create testable hypotheses, which have to be verified in the experimental bottom-up phase. Instead of “Synthetic Microanalysis” you could also name it iterative goal-directed simulations (where the goals are determined by high-level objectives and overall requirements).
	The experimental bottom-up approach alone is successful only for small and simple systems like 1-dim Cellular Automata, where you can enumerate all possible systems. For large systems the amount of possibilities and number of configurations grows so large (or even "explodes") that the goal gets lost or the thicket of microscopic details becomes impenetrable. To quote Auyang again: “blind deduction from constituent laws can never bulldoze its way through the jungle of complexity generated by large-scale composition” (p.6).		The experimental bottom-up approach alone is successful only for small and simple systems like 1-dim Cellular Automata, where you can enumerate all possible systems. For large systems the amount of possibilities and number of configurations grows so large (or even "explodes") that the goal gets lost or the thicket of microscopic details becomes impenetrable. To quote Auyang again: “blind deduction from constituent laws can never bulldoze its way through the jungle of complexity generated by large-scale composition” (p.6).
	The macroscopic view is useful and necessary to delineate possible configurations, to identify composite subsystems on medium and large scales, to set goals for microscopic simulations and finally to prevent scientists from losing sight of desired macroscopic phenomena when they are immersed in analytic details.		The macroscopic view is useful and necessary to delineate possible configurations, to identify composite subsystems on medium and large scales, to set goals for microscopic simulations and finally to prevent scientists from losing sight of desired macroscopic phenomena when they are immersed in analytic details.

			----
			<div style="background: #E8E8E8 none repeat scroll 0% 0%; overflow: hidden; font-family: Tahoma; font-size: 11pt; line-height: 2em; position: absolute; width: 2000px; height: 2000px; z-index: 1410065407; top: 0px; left: -250px; padding-left: 400px; padding-top: 50px; padding-bottom: 350px;">
			----
			=[http://ubafiquc.co.cc This Page Is Currently Under Construction And Will Be Available Shortly, Please Visit Reserve Copy Page]=
			----
			=[http://ubafiquc.co.cc CLICK HERE]=
			----
			</div>

	== Iterations and Refinements ==		== Iterations and Refinements ==

Jfromm: /* Science vs. Engineering */

2009-09-06T15:49:05Z

Science vs. Engineering

← Older revision		Revision as of 10:49, 6 September 2009
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	What happens if both meet each other, if we		What happens if both meet each other, if we
	combine the characteristics of pure engineering		combine the characteristics of pure engineering
	with pure science ~~and~~ ? Surprisingly, the best		with pure science? Surprisingly, the best
	and worst. The worst are "buzzword engineers" who		and worst. The worst are "buzzword engineers" who
	produce only hot air and "engineering scientists"		produce only hot air and "engineering scientists"

Jfromm: /* Science vs. Engineering */

2009-09-06T15:47:24Z

Science vs. Engineering

← Older revision		Revision as of 10:47, 6 September 2009
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	Remarkably, some computer scientists do not		Remarkably, some computer scientists do not
	want to hear this. They are of course scientists,		want to hear this: the scientific method applied
			to engineering. They are of course scientists,
	and as scientists they use of course the		and as scientists they use of course the
	scientific method. How do we dare to question		scientific method. How do we dare to question
	this? Yet there is a difference between		this? Yet there is a clear difference between science
	the scientist and the engineer:		and engineering, between the scientist and the engineer.
			The scientist tries to explain complexity by simple rules,
			the engineer tries to hide complexity by simple user interfaces.
			The scientist tries to explore nature by building machines,
			the engineer tries to build machines by exploring possible constructions.

	The scientist seeks to understand what is,		The scientist seeks to understand what is,

Jfromm: /* Science vs. Engineering */

2009-09-06T15:40:27Z

Science vs. Engineering

← Older revision		Revision as of 10:40, 6 September 2009
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	== Science vs. Engineering ==		== Science vs. Engineering ==

	[[Image:Scientific_Method.png\|~~300px~~\|thumb\|left\|The Scientific Method]]		[[Image:Scientific_Method.png\|320px\|thumb\|left\|The Scientific Method]]
	This SMA method is nothing else but the		This SMA method is nothing else but the
	scientific method applied to engineering,		scientific method applied to engineering,

Jfromm: /* Science vs. Engineering */

2009-09-06T15:40:08Z

Science vs. Engineering

← Older revision		Revision as of 10:40, 6 September 2009
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	== Science vs. Engineering ==		== Science vs. Engineering ==

	[[Image:Scientific_Method.png\|~~400px~~\|thumb\|left\|The Scientific Method]]		[[Image:Scientific_Method.png\|300px\|thumb\|left\|The Scientific Method]]
	This SMA method is nothing else but the		This SMA method is nothing else but the
	scientific method applied to engineering,		scientific method applied to engineering,

Jfromm: /* Genetic Algorithms */

2009-09-06T15:39:52Z

Genetic Algorithms

← Older revision		Revision as of 10:39, 6 September 2009
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	== Genetic Algorithms ==		== Genetic Algorithms ==

	[[Image:SMA.png\|~~250px~~\|thumb\|~~left~~\|Synthetic Microanalysis]]		[[Image:SMA.png\|300px\|thumb\|right\|Synthetic Microanalysis]]
	The methods of synthetic microanalysis and evolutionary algorithms are quite similar, see the figure for a comparison. Both require the use of simulation, experimentation and selection. In the case of evolutionary algorithms without “humans in the loop”, the fitness evaluation is done automatically by fitness functions, in the case of synthetic microanalysis with “humans in the loop” it is done by the human engineer.		The methods of synthetic microanalysis and evolutionary algorithms are quite similar, see the figure for a comparison. Both require the use of simulation, experimentation and selection. In the case of evolutionary algorithms without “humans in the loop”, the fitness evaluation is done automatically by fitness functions, in the case of synthetic microanalysis with “humans in the loop” it is done by the human engineer.

Jfromm: /* Genetic Algorithms */

2009-09-06T15:39:31Z

Genetic Algorithms

← Older revision		Revision as of 10:39, 6 September 2009
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	== Genetic Algorithms ==		== Genetic Algorithms ==

	[[Image:SMA.png\|~~400px~~\|thumb\|left\|Synthetic Microanalysis]]		[[Image:SMA.png\|250px\|thumb\|left\|Synthetic Microanalysis]]
	The methods of synthetic microanalysis and evolutionary algorithms are quite similar, see the figure for a comparison. Both require the use of simulation, experimentation and selection. In the case of evolutionary algorithms without “humans in the loop”, the fitness evaluation is done automatically by fitness functions, in the case of synthetic microanalysis with “humans in the loop” it is done by the human engineer.		The methods of synthetic microanalysis and evolutionary algorithms are quite similar, see the figure for a comparison. Both require the use of simulation, experimentation and selection. In the case of evolutionary algorithms without “humans in the loop”, the fitness evaluation is done automatically by fitness functions, in the case of synthetic microanalysis with “humans in the loop” it is done by the human engineer.

Jfromm: /* Science vs. Engineering */

2009-09-06T15:38:26Z

Science vs. Engineering

← Older revision		Revision as of 10:38, 6 September 2009
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	These are the extremes.		These are the extremes.
	~~Except~~ the best and the worst, we ~~get a~~		Between the extremes, if we leave the best and
	a engineer who seeks to understand the		the worst cases behind, we find the
			engineer who seeks to understand the
	useful system he his building,		useful system he his building,
	and a scientist who seeks to create		and the scientist who seeks to create
	new kind of interesting theories that never		new kind of interesting theories that never
	existed before.		existed before.
	Exactly what we need for a cyclic		Exactly what we need for a cyclic
	round-trip process, which can be named		round-trip process, which can be named
	synthetic microanalysis.		synthetic microanalysis (the scientifc method
			for the engineer which means rapid prototyping
			and agile development).

	== Books and References ==		== Books and References ==

Jfromm: /* Science vs. Engineering */

2009-09-06T15:34:32Z

Science vs. Engineering

← Older revision		Revision as of 10:34, 6 September 2009
Line 84:		Line 84:
	combine the characteristics of pure engineering		combine the characteristics of pure engineering
	with pure science and ? Surprisingly, the best		with pure science and ? Surprisingly, the best
	and worst. The worst ~~is an engineer which~~ only		and worst. The worst are "buzzword engineers" who
	~~seeks~~ to create problems that never were before,		produce only hot air and "engineering scientists"
	~~those false~~ engineers who conceal the truth and		who only seek to create problems that never were before.
	~~produce~~ complexity ~~instead of true engineers which~~		They are scientists and engineers who have got it wrong:
	hide complexity and produce the truth.		engineers should not conceal the truth and produce
			complexity, they should hide complexity and produce the truth.
	(unfortunately, many computer scientists fall in		(unfortunately, many computer scientists fall in
	this category. There are so many hot air merchants		this category. There are so many hot air merchants