The Introduction

The science of the artificial by Simon A Herbert encapsulates the idea that we live in an artificial world, designed, and engineered for human construction to achieve goals and solve problems that fundamentally aid the development of society. We can explore the complexity in most systems that are seen within the modern day from cognitive psychology, biological systems, computational design, our economic reality and how we as humans behave and organise our society for modern living. The word ‘artificial’ is used throughout the book which is in reference to the form elements take and how they react and adapt to the environment in which they sit. We are a depiction of our environment and it can be seen in the way we design the artificial. Simon Herbert explores the advances of the science of design and how complexity and the natural laws of nature can apply, and be used as a tool to explain why we design and the way people interact with their environment. Simon Herbert compares how the design of computational programmes can explain fundamentally how humans think, act and intelligently react to the world around us.

Figure 1 - A farm is an artificial construct despite replicating the qualities of nature.

Chapter 1 - Understanding the natural and artificial world

We live in an artificial world that is devised and constructed for human benefit. We create artificial constructs, that is seen within our architecture, our agriculture and within everyday objects we use within our homes. We create through our imagination that is a direct imitation of our natural environment. Throughout the real world, we can characterise the way we behave through the inner and outer environment. We can define the inner as something that functions internally, something that is organised of natural phenomena, that is capable of achieving goals in a variety of outer environments. The outer can be defined through why something functions based on the place in which it sits. (Herbert, A, 1996). These two systems are in collaboration together that can be used to predict and define behaviour and that most of our behaviours can be explained through adaptation to the outer environment. In computation, simulations can be used as a tool to predict these behaviours and take this further by manipulating control over the natural environment to experiment with how humans adapts and fundamentally solve problems in unique environments. (Herbert, A, 1996). Using the description of the inner and outer environments and the complexity of computational methods, this can be used to define why we act and why our society is structured/organised the way it is.

Chapter 2 - Economic Rationality: Adaptive Artifice

‘Economics exhibits in the purest form the artificial of human behaviour’ (Herbert, A, 1996). The world of economics is a direct adaption of humans reacting to the environment and an explanation of how we have a computational ability to achieve our goals and make rational decisions through models and processes. The operation of the real world would be impossible without computers (Herbert, A, 1996). Simon Herbert clarifies that within economics and within decision making in general, we have an equilibrium between finding the optimal and satisfactory solution (Herbert, A, 1996). This can explain human judgement and can be aided by the use of computational methods such as artificial intelligence and operational research that is perceived through procedural rationality.

The idea of finding a satisfactory solution is built into our inner environment meaning that finding the optimal is limited by this. We are bounded by rationality, loyalty, society, and by the economic interests of others in our decision making, something that computers are not affected by. The diagram above displays the prisoner’s dilemma that analysis’s the level of satisfaction created from the consequences of the decision. In relation to economics, the theory of games is a realistic scenario that can explain rational action in the interest of two parties in collaborative efforts to achieve satisfaction.

Figure 3 - The ants path is not because of the complexity of the ant, but of the environment.

Chapter 3 - The psychology of thinking: Embedding Artifice in Nature

Why is an ant’s path not straight and simple? (Herbert, A, 1996). Simon Herbert considers the path of an ant in its efforts to obtain food from a beach environment. The ant’s path is unique but it isn’t a product of the complexity of the ant, but a complexity of the environment that the ant finds itself in and that these actions are very similar to that of a humans. Most of our reactions and decisions are passed on from a reflection of the outer environment. We make decisions and solve problems based on the information around us.

Simon Herbert delves deeper into the process of problem solving and states it is described as a ‘search through a vast maze of possibilities that describe the environment’ (Herbert, A, 1996). Search strategies are used, such as trial and error, to find solutions to complex issues, but this is limited by the capacity/accuracy of memory. We do have a rapid ability to access storage that is learnt and transferred from short term memory to long term. Cognitive psychology can explain the limits and processes of an adaptive system and that human behaviour is a learnt technique that is acted through memory of our experiences of the external environment.

Figure 4 - Diagrams explaining the process of the trial and error and the transfer of information from short term to long term memory.

Chapter 4 - Remembering and learning: Memory as Environment for Thought

Chapter 4 delves deeper and explores the complexity of memory. The human and computational memory is similar to a library with an unlimited capacity for information and rapid access to this information. Aspects of association and meaningfulness aid this retention, this explains why chess players are able to remember moves and strategies because humans create lists and hierarchies to organise memory. Within specific domains information is associated with familiar patterns of what to do and how to use memory, to again solve problems, which involves thinking immediately – recognising the situation and deciding the appropriate action. (Herbert, A. 1996). The knowledge and skill of retaining information resides in the external environment of the long term memory which draws upon general processes that control problem solving search processes and recognition. Understanding domains requires previous knowledge of that domain. More knowledge means more storage, larger size, more organisation and more complex memory. (Herbert, A. 1996). Any changes within the memory is a direct result from the environment, this change is known as learning. This process of learning can be aided through discovery processes as stated in the previous paragraph. We create representations for scenarios and search for solutions. These solutions can be aided through the retrieval of previous learnt information stored within the memory. This is highly reflective of computational processes.

Figure 5 - Computers have the ability to analysis millions of variations using brute force computation.

Chapter 5 - The science of Design: Creating the Artificial

Simon Herbert outlines the development of the science of design using computational logic, conditioning and the idea of the satisficing. Science is known as the study of the natural and engineering explores the artificial. The Science of design combines the natural laws of the real world and the process of design which uses computation to aid the procedure (Herbert, A. 1996). Throughout the book, there is constant reference to how humans react to their environment. The adaptation of the inner and outer is defined by utility function which is a function of variables supplied by the parameters of a number of constraints. An example of this is a standard real world maths problem that considers all possible pathways that meet the constraints of the outer. (Herbert, A, 1996). We can solve issues of design by utilising computational methods. Computer problem solving programs can use brute force computation by analysing millions of variations to evaluate steps or pathways for decision making. The computer also has efficient techniques that find optimal courses of action in real scenarios. (Herbert, A, 1996).

The design process uses techniques to find a generalisation of alternatives and options, which is tested using a whole array of constraints and requirements via computational generators that is the decomposition of the design issues and the tests in order to find the consequences of the actions. For example, the idea of the satisfaction can explained through the cost-benefit analysis of providing design resources when participating in a project. (Herbert, A, 1996)

Figure 6 - Qualities of a social architecture that creates a efficient society.

Chapter 6 - Social Planning: Designing the Evolving Artifact

The evolution of the artificial can be evaluated through the theories around social planning and how we plan for the future. Plans are based on social/political arrangement of the physical environment and construction is a success of human planning. (Herbert, A, 1996). The problem with social design is that there is no criteria to city planning. The society we live in is made fit for human inhabitation and planners aim to influence behaviour to overall benefit the behaviour of society, and the organisation of the design is to achieve goals and encourage social interaction. We design in order to provide a satisfactory future that has sufficient food, health, shelter, and the growth of knowledge and the goals in design is to provide efficient function and provide longevity. (Herbert, A, 1996)

We design for the future. Design goals can be limited by the ability to predict what the future holds. As designers we need to consider the next steps and leave behind a set of foundations of knowledge for our successors in society to create new alternative paths. ‘Designing is mental window shopping. Purchases do not have to be made to get pleasure from it’. (Herbert, A, 1996)

Figure 7- Chaotic systems with unpredictable behaviours.

Chapter 7 - Alternative Views of Complexity

There are alternative views of complexity. It is associated with chaos, catastrophe, generic algorithms and cellular automata. Catastrophe theory explores the classification of nonlinear dynamic systems in relationship to a set of behaviours. The explanation of catastrophe is a special system that any change in the parameters can lead to instability, which correlates to systems in nature. Chaos systems are systems with unpredictable behaviour because the systems initial conditions are disturbed. Examples of this are the weather systems and the orbit of the planet (Herbert, A, 1996). The theory of chaos is more recognised because it is more predictable. This means that the consequences can be managed and prepared for.

We can also apply the emergence of complexity through the computational view of evolutions. Generic algorithms can be represented by a list of vectors. The vectors of evolution and the complexity of this is a Darwinism approach of survival of the fittest and is measured by the amount of offspring and reproduction of favourable genes. We can use computational methods to simulate natural selection and to analysis how fitness will change. This can lead to the production of evolution self-reproduction systems. Overall Simon Herbert evaluates complexity stating that ‘complexity is too general, this is not the study of a specific complex structure but the phenomena of complexity’. (Herbert, A, 1996)

Figure 8- Hierarchies are split into subsystems with subsystems that are subordinate to authority.

Chapter 8 - The Architecture of Complexity: Hierarchic Systems

A complex system is defined by interactions. The structure of complexity can be explained in terms of a hierarchy form that is a subsystem, that is then divided into more subsystems and it is the properties within the hierarchical systems which can be used to analysis behaviour. This theory of complexity can be associated with society. The subsystems are subordinate to authority of a higher subsystem and it is the interaction and the intensity of these interactions that define a social hierarchy. We can see hierarchies in most complex systems. They can be referenced in organisms on a microscopic scale to a macroscopic scale of planets. Overall, Simon Herbert identifies complex systems to be an evolution of similar simple systems. This can explain why we have flat hierarchies such as the system of diamond that is far less structured in a hierarchical sense compared to society.

The interactions within a hierarchy can be defined as weak or strong. There can be strong communication with elements that are interlinked closely together compared to weak interaction between other components. Each component work together but can be unaware of other components without any effect. If one component changes within the hierarchical system it does however have an effect on other components. By analysing these structures and hierarchies we can simplify how humans will fundamentally behave because there is an organisation that directly links to groups and components working for the same goals.

The Conclusion

In summary, there are a vast amount of theories, comparisons and examples that express the idea of complexity in our artificially constructed human world. Through nature, our cognitive psychology and the planning of society, we can predict and analysis human behaviour/problem solving and how this fundamentally is defined by the outer environment and only limited by our ability to perceive this through our inner environment. This not only applies to human behaviour but can be applied on the micro scale of organisms and the macro scale of weather and the planet in which we inhabit. The computational methods described in the book, through the similarity of the function of processing, programmes and simulation, we can use this as a prediction to human behaviour and develop our understanding of problems presented within the world. The book takes the meaning of the world artificial and creates connections between how the artificial is constructed, why it is and what makes us as human want to design.

References

Herbert, S., 1996. The Science of the Artificial. 3rd ed. London: MIT Press.

Figure List

Figure 1 - National Geographic, n.d. Farm. [image] Available at: <https://www.nationalgeographic. com/environment/future-of-food/organic-farming-crops-consumers/> [Accessed 17 November 2020].

Figure 5 - Herbert, S., 1996. The Science of the Artificial. 3rd ed. London: MIT Press.

Figure 6 - The Philadelphia inquistitor, n.d. Tornado. [image] Available at: <https://www.inquirer. com/weather/tornado-outbreak-deaths-historic-pennsylvania-weather-may-1985-20200531. html> [Accessed 17 November 2020].

The sciences of the artificial: A review by Jay Sinclair