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The paper aims to relate the four modes of coexistence of goal-orientated systems – conflict, hierarchy, the niche and cooperation – to static behavioral descriptions of social systems.

Analyzing the options for interactions of goal-orientated systems leads the four modes of coexistence. These show certain behavioral characteristics. Searching for these characteristics in selected behavioral descriptions in organizational theory, sociology, political science and system science allows relating them to certain modes.

In organizations, the four modes of coexistence show in general (Cameron and Quinn) and dyadic interaction patterns (Argyris). In sociology, they show in educational schemes preparing for specific modes (Bernstein) and in different exchange patterns (Polanyi). In political theory, the four modes of coexistence show in preferred policies of parties, in institutional development, in oligarch power politics (Winters) and in external policy. In system science, they show in problem solving strategies (Jackson) and “mindscapes” (Murayama).

The analysis is restricted to static patterns leaving out dynamic developments, for example, in networks.

Together with previous investigations, the paper shows how behavioral descriptions ranging from psychology to politics correspond to the four modes of coexistence, and it suggests interrelations and alignments of the modes via various levels of societal organization.

The four modes of coexistence provide an unequivocal theoretical framework that allows finding parallels in behavioral descriptions ranging from psychology via sociology to politics.

The paper analyzes how the goal-setting of two individuals or social units A and B determines the utilities, which the two parties can gain in a dyadic interaction.

The analysis is based on a dyadic utility space representing the utilities of A and B along the x and y axis, respectively. In this space the goal-orientation of an individual action is mapped with a vector, where its angle shows the combination of utilities of A and B aimed at, and the length corresponds to the strength of an action.

Analyzing actions of A and responses of B in the dyadic utility space allows (a) calculating single and joint utilities, (b) identifying the narrow range of equal utility for both and (c) identifying four vectors for maximizing different combinations of utility. Studying combinations of these four vectors in a 4 × 4 ‘Interaction - Utility Matrix’ shows how the goal-setting of A for a prime action widely predetermines, before a response of B, the realizable dyadic utility for both.

The dyadic utility space allows illustrating any dyadic interaction: It shows all possible dyadic “payoffs” investigated in game theory; it allows studying repeated exchange and resulting accumulation of utility; it allows mapping power relations.

The paper shows the interrelation between cybernetic principles of control, goal-orientated human behavior and the utility concept of social sciences. And it allows rejecting Adam Smith’s 18th myth of an “invisible hand”.

The purpose of the paper is to analyze cybernetic necessities of output‐side attention directing systems, i.e. how systems can decide to act towards one of various inputs.

Complex pattern recognition and sequence learning systems may recognize more than one pattern and deliver more than one output at a point in time. Therefore, they require an output‐side attention directing system to decide to act towards just one pattern. The necessary cybernetic structures of such systems are analyzed using a functional approach.

An output‐side attention directing system has to evaluate the effect of current observations (patterns, sequences, etc.) on highest level goal‐values (in a living system these are existential goal‐values like a body temperature or energy supply). Measure of this effect is the degree of goal‐approximation towards these goal‐values. This measure can either be preprogrammed for some patterns or sequences, or has to be determined in trial and error processes for new patterns or sequences learned by the system.

The paper shows the cybernetic necessities of the development of the “know how” of sequence learning systems in time, starting with default behavior, via learning new patterns and sequences, and trial and error to develop goal‐orientated actions towards them, until finally the achieved results enable experience based directing of attention.

The paper shows basic cybernetic structures and functions for output‐side attention directing systems required for all complex pattern recognition and sequence learning systems.

The purpose of this paper is to analyze the main differences in the cybernetic structures necessary for elementary anticipation, understood as anticipation of the repetition of one known pattern, and complex anticipation, understood as anticipation of the repetition of known sequences of patterns.

A functional cybernetic approach is used to develop the necessary additions to an elementary anticipatory system, so that it can provide standards for anticipated sequences containing seven single patterns or “chunks”.

A subsystem for the anticipation of sequences is developed that is able to: identify the beginning of known sequences; search for different known sequences containing that beginning; and decide to use later patterns of such a sequence as standards for anticipated patterns. Deciding to actually use such patterns for anticipation requires an additional subsystem to switch between the feedback pattern recognition and the feedforward anticipation mode.

The paper shows how complex anticipation can be developed from elementary forms by adding highly parallel structures that apply the same underlying principles; and it emphasizes epistemological demands for the structure and the data organization that have to be fulfilled, so that anticipation of the repetition of sequences becomes possible.

The paper illustrates the complexity of the anticipation of sequences and it provides the base to analyze more complex forms of specifically human thinking.

The purpose of this paper is to analyze how elementary anticipation, understood as anticipation of the repetition of one known pattern, can emerge out of sequence learning and how it can contribute to the behavioral options of goal‐oriented systems.

A functional approach is used to develop the necessary cybernetic structures of a subsystem for sequence learning that can additionally provide standards of anticipated patterns for future pattern matching. Based on that it is analyzed, how a goal‐oriented system can use the information about the actual occurrence of an anticipated pattern.

A subsystem for elementary anticipation of single patterns builds on sequence learning and requires additionally a structure: first, to unequivocally identify the beginning of known sequences just from their first patterns; and second, to decide to use a latter pattern of such a sequence as standard for an anticipated pattern. Deciding to actually use such a pattern for anticipation requires an additional subsystem to switch between the feedback pattern recognition mode and feedforward. Then the occurrence of such an anticipated pattern allows immediate recognition and action.

The paper shows a necessary evolution of cybernetic structures from pattern recognition via sequence learning to anticipation; and it shows, too, a necessary evolution in the cognitive development of individual systems. In the simple anticipatory structures analyzed here, only known patterns, that are part of a known sequence, can become anticipated patterns.

The paper places elementary anticipation of single patterns in an evolutionary development based on pattern recognition and sequence learning. It provides the base to analyze more complex forms of anticipation.

The purpose of this paper is to investigate necessary cybernetic structures that allow complex adaptive systems to develop system‐specific behavior.

Following Holland's concept of “adaptive agents”, it is argued that the development of system‐specific forms of goal‐oriented behavior requires a decision to deviate from some default behavior and to trigger any new one, and a mechanism to evaluate the goal‐orientation of this new behavior. Using a functional approach cybernetic structures are developed that are able to carry out these two tasks. Then these structures are added as subsystems to the structure of a simple one‐level adaptive system.

The paper finds that a hierarchical adaptive system can recognize with a higher level controller, if lower level decisions lead to an insufficient degree of goal‐approximation and can use preprogrammed higher level decisions to intervene on the lower level to trigger new system‐specific actions. An additional controller can evaluate the “success” achieved with these new actions and can select the “best” actions found, i.e. the behavior leading to the highest degree of goal‐approximation.

The paper shows necessary cybernetic structures that are seen as core of all complex adaptive systems able to develop system‐specific behavior. It is suggested that the underlying basic concept of “success” understood as a degree of goal‐approximation holds for any adaptive, learning or otherwise improving endeavor.

The paper is the second in a series of three on a cybernetic theory distinguishing system capable of preprogrammed adaptation, system‐specific adaptation, and learning. It shows necessary cybernetic structures that a system can develop individual actions.

The purpose of this paper is to investigate different cybernetic structures of simple adaptive systems and their cognitive and behavioral options.

Using a functional approach, two basic forms of adaptive systems are constructed, which process data on one level respectively two hierarchical levels. Based on that complex combinations of such one‐level and hierarchical structures are investigated.

It is shown how different cybernetic structures enable simple forms of adaptive behavior. A basic blueprint for the controller structure of animal species is derived from them, with a simple “brain” and a unit for “motion control” as subsystems. Four paths of evolutionary growth are identified that allow a widely independent development of these subsystems.

The paper provides a typology of simple adaptive systems and discusses the forms of behavior they can develop with preprogrammed – i.e. evolutionary given or technically programmed – decision‐rules. It discusses the requirements that these decision‐rules can form models enabling adaptive behavior. It is suggested that these requirements hold for the models of more complex adaptive systems, too.

This paper is the first in a series of three on a cybernetic theory distinguishing systems able of preprogrammed adaptation, system‐specific adaptation, and learning.

The purpose of this paper is to analyze how sequence learning can build on pattern‐recognition systems and how it can contribute to the behavioral options of goal‐oriented systems.

A functional approach is used to develop the necessary cybernetic structures of a subsystem for sequence learning, that can recognize patterns, register patterns occurring repeatedly and connect these to sequences. Based on that it is analyzed how goal‐oriented systems can use information about reoccurring sequences.

A subsystem for sequence learning basically requires pattern recognition and it needs a structure for the directed connection of single standards for pattern matching to standards for sequences, given that it can learn both new patterns and new sequences. Such a subsystem for sequence learning may recognize a certain pattern and with that the end of a certain sequence. So it may deliver more than one output signal at a point in time, and therefore needs additionally a subsystem for directing attention.

The paper analyses the principles of an “associative” way of connecting standards for pattern matching to standards for sequences. Also it shows the cybernetic necessity of an attention directing system that has to decide how to deal with the multiple outputs of a subsystem for sequence learning, i.e. to decide to act either towards a pattern or a whole sequence.

The paper investigates basic mechanisms of sequence learning and its contribution to goal‐oriented behavior. Also, it lays the base for an analysis of attention directing systems and anticipatory systems.

The purpose of this paper is to analyze how pattern recognition can contribute to the behavioral options of a goal‐oriented system.

A functional approach is used to develop the necessary cybernetic structures of a pattern recognition unit that can store observations as new standards for pattern matching by itself and can later apply them to recognize patterns in incoming sensor data.

Combining such a structure for pattern recognition with a feedback system shows that the resulting system can only deal with known patterns. To deal with novel patterns this structure has to be added to an adaptive system that can develop system‐specific behavior. Such a system has to able to initiate a trial and error process to test new behavior towards new patterns and to evaluate its effect on the highest, existential goal‐values of the system.

A system with a pattern recognition unit that can set new standards for pattern matching by itself is identified as the point of departure where not‐programmable and unpredictable individual behavior starts. Dealing with newly‐recognized pattern requires individual behavioral solutions and a system‐specific evaluation of the achieved results in relation to the highest goal‐values of the system. Here internal “emotional” criteria to select behavior emerge as a cybernetic necessity.

The paper is the third in a series of three on a cybernetic theory distinguishing system capable of pre‐programmed adaptation, system‐specific adaptation and learning. It determines the cybernetic starting point of individual psychology.

The purpose of this paper is to present decision making as the decisive activity of controllers, necessary to correct deviations from a goal value in controlled systems by deciding on goal‐oriented actions.

Using a functional approach it is shown how an increasing complexity of controller structures follows an increasing ability to make more complex decisions. Two applications are used to analyze that in detail: first, the controller structures necessary for Miller's living systems and for Beer's viable systems are presented in one comparable scheme. Second, a complex controller structure illustrates the basic requirements for a brain.

Analyzing necessary decisions in Beer's viable systems it is shown how the elementary decisions found in feedback systems can be used as a first approximation for decision making. Hence, it is shown how principles of decision making determine the development of complex controller structures.

The paper provides basic analytic tools to understand the interrelation between controller structures and the content of the decision these structures can make. It shows four different evolutionary paths from feedback systems towards brain structures. Also it emphasizes the role of goal values in decision making and their importance especially for social systems.

The paper seeks to present cybernetics as a science of structures that enable certain decisions. It is suggested that making explicit the goal orientation of decision making gives cybernetics an additional relevance for the social sciences.