This is an excerpt from my Master’s Thesis: The Relevance of Emergence in Human-centered Design.
To download the entire Thesis, please go to: My Master’s Thesis

 
In computer games or animations, how do animators make a complex motion of a flock of birds? Rather than outlining a pathway for each individual bird, the animators use computer simulation of the aggregate motion of a swarm to mimic those in nature. The simulation uses a model in which each bird “chooses” and “navigates” its own course based on its local interactions with the dynamic environment (i.e., other birds around it). The behaviors of the birds that are seemingly conscious are only possible when a set of simple rules is programmed into each individual bird. These simple rules include: avoid collision with nearby birds, match the velocity of nearby birds, and stay close to nearby birds (Reynolds, 1987).

From these simple rules, the aggregate motion of the birds creates a complex group behavior. This complex behavior is called emergence. Studying emergence involves looking at the interactions of many different actors (or agents). In the example above, the actors are the birds. However, actors may be ants, robots, and even people. This thesis is going to explore design and emergence. However, rather than studying automatons (such as the computer modeled birds or robots), this thesis is interested in exploring emergence with people, i.e., from the human-centered perspective.

This chapter will start with the general concepts of human-centered design and explore the principles of the theory of emergence and the emergent processes from different fields of study. Then, I will define aspects of human-centered design and theories of emergence into common terms used for this writing.
 
General Concepts of Human-Centered Design
In querying about design from a human-centered perspective, inevitably the question of “what it means to be human” is implied. In the design disciplines often times, the usage of the terms user-centered design and human-centered design are synonymous. However, there is a distinction to be made between user-centered design and human-centered design. User-centered design focuses on the external aspect, namely, the behaviors of people. Usually, user-centered research conducts user-testing on specific products or services in diverse contexts, such as retail environments, mobile apps, information design, etc. Thus, the categorization of users includes customers, product users, audience, and so on. Human-centered design, on the other hand, does not have the specific institutional functionaries (e.g., consumers, workers, users, personas, etc.) of user-centered design. Rather, human-centered design looks at the internal aspects of people, such as intentions, capabilities, and creativity. In a sense, user-centered design is more evaluative, and human-centered design can be more generative. The simple distinctions could be summed up as this: User-centeredness asks how people behave, whereas human-centeredness asks how people could behave.

Looking from the perspective of human potentials and capabilities is deeply rooted in the philosophy of co-creation, that is the act of creativity “shared by two or more people” (Sanders & Stappers, 2013, p. 25). The co-creation mindset is the starting point to introduce a new perspective in the process of design development, i.e., participatory design. Co-creation is an umbrella term that covers participatory design or co-design (this thesis will use the terms participatory design and co-design interchangeably). ¹ Co-design is a process of design, in which “the creativity of designers and people not trained in design working together in the design development process” (Sanders & Stappers, 2013, p. 25).

The inclusion of people into the design process and utilizing human creativity have been the main premises of participatory design. These premises make up the core beliefs of human-centeredness that are foundational to this approach in design:
1. All humans are creative.
2. Co-designing empowers collective creativity.

There are many different types of creativity, however, this thesis is only interested in people’s everyday creativity. Such daily creativity manifests itself in many forms that we often take for granted, for example, human language use. Sanders and Stappers elaborated Margaret Boden’s notion of P-creative, or psychological creativity, where “someone borrows an idea from one domain and applies it to another.” This type of creativity is “everyday creativity,” which is possessed by everyone (Sanders & Stappers, 2013, pg.38).

Hieronymi (2013) defined creativity as a process “when something new is created that is useful or has other kind of value.” He expressed a similar view that such creativity is not exclusive to the gifted. Rather, it is “a fundamental aspect of a human life.” Much like how human-centered design looks at the internal aspects of people, such as human capabilities, Hieronymi was also concerned with people’s creative potential. He asserted that the process of the “creation of novelty” itself was relevant to emergence.

Collaboration does not always ensure success, however, human creative potential can heighten when people (especially from different disciplines) work together. The collaborative efforts of this thesis approach are deeply influenced by participatory design. Participatory design has always been tied with democratic principles (direct, instead of represented). ² The involvement with people in participatory design has its roots from Scandinavian cultural and political ideals related to their work with trade unionism in the 1960s and 1970s.

According to Gregory (2003), the Scandinavian participatory design movement stemmed from the post-war political movements, where the ideas of workers’ autonomy to govern themselves in decision-making took forms as “co-determination” with the unions to “improve the quality of working life.” It is this kind of democratic principle that makes participatory design distinct from user-centered design. Participatory design efforts go beyond commerce-oriented issues. They also encapsulate social relations in the broader “context of democratisation of society” (pg.64). Thus, participatory design is inherently human-centered.

Sanders & Stappers (2014) have a vision for the design disciplines towards such change. In their Three Slices in Time, they described that the design disciplines have gone through different permutations over the past three decades from “designing for people to designing with people.” These democratic mindset and democratization efforts are fundamentals in the participatory design approach. The core philosophy outlined above — of human creativity and co-designing — that will tie in the relevance of human-centered design to emergent systems when the focus shifts to designing “by people” (the third slice of time).

General Principles of Emergence
The concept of emergence is used in different disciplines and is studied vigorously in the sciences and philosophy. Emergence covers a wide range of subject matters from the study of consciousness, behaviors, to the study of ecology. Before jumping too deep into the phenomenon of emergence, we must first be able to make sense of what a system is.

A system could be defined as “an interconnected set of elements that is coherently organized in a way that achieves something” (Meadows, 2009, pg.11). According to Donella Meadows, a system should at least consist of: Elements, Interconnections, and Function or Purpose. Elements are the basic building blocks—parts that make up the sum of the whole. Interconnections are the “relationships that hold the elements together.” Depending on the level of the complexity, the purpose of a system may be more obvious in some than others.

One could look at a system from a simple or complex perspective. Simple systems are usually linear and predictable (Figure 3.1). A classic example is the process of production of a pin. Each step of the process of making a pin is very distinct: “One man draws out the wire, another straights it, a third cuts it…” and so on, until a pin is produced (Smith, 1993, pg.12). The materials for making the pin are the basic elements (or components). The flow from one step to another is governed by certain instructions that create the interconnectedness in the system. No matter how many times the process is operated, the final product serves the purpose of producing a pin. Such consistency and predictability are the consequence of the system in place. John Stuart Mill (1889) called this phenomenon the “composition of causes,” in which “the joint effect of several causes is identical with the sum of their separate effects [italics mine]” (p. 243).

 

Figure 3.1. A simple system that is linear and produces results as intended.

There are entities that may look like a system, however, they do not have any particularities (like functions or purposes) that would qualify those entities as a system. For example, “Sand scattered on a road by happenstance is not, itself, a system. You can add sand or take away sand and you still have just sand on the road” (Meadows, 2009, pg.12).

More complex systems have processes and purposes that are difficult to identify (Figure 3.2). The university, for example, is a complex system of interrelated tangible and intangible elements. The tangible elements may be buildings, students and faculty members, whereas the intangible elements maybe school spirit, mission of the university, and so on (Meadows, 2009, pg.13).

 

Figure 3.2. Complex systems are usually nonlinear.

Complex systems are exceedingly difficult to see (and make sense of) from a single point of view. Take the aforementioned example, a student may identify a shortcoming in a particular instance of the educational experience he encountered. However, there are a lot of invisible elements or factors that are not accounted for. The certain method of teaching may be enforced by the administrative constraints that are outside of the faculty’s control. Furthermore, the elements could behave in certain ways that not only do not correspond to the rules, but the consequences are unexpected as well.

Sometimes the components self-organize and create new behaviors or functions. Such systems have the properties of emergence. Again, Mill pointed out that, contrary to the “composition of causes” that the joint of the parts is “identical with the sum”, in emergent systems “the juxtaposition of those parts in a certain manner, bear no analogy to any of the effects which would be produced by the action of the component substances considered as mere physical agents” (1889, p. 243). In other words, the whole “is more than the sums of its parts [italics mine]” (Meadows, 2009, pg.12).

In order for the components to produce any actions, there needs to be an agency. An agency is the capability to act. In other words, an agency gives the components the capability to act. In the example at the beginning of this chapter, the algorithms (collision avoidance, synchronicity, and proximity) embedded in individual birds are the agencies. Thus, the birds are the agents in the computer simulation. Agents (or actors) are the entities that are able to act. However, not all components are agents. The aforementioned birds are agents because of their particular innate properties — the algorithms bestowed by the programmer — that allows them to act by themselves. In different systems, a component that is not able to act by itself could not be said to be an agent (thus, not having an agency). For example, a pen is a component that could only write because of the person (an agent) using it as a writing tool.

Emergent phenomena are quite ubiquitous in many complex systems and they are studied in many different domains of the sciences and humanities. Those different fields of study follow the underlying principle of emergence, yet have subtle variances in the way they define emergent phenomena (for example, ontological and epistemological) in respect to their disciplines. One important consequence of the proposed variety of the theoretical description of emergent phenomena is the division of the concept of emergence into two general categories. One is called strong emergence and the other weak emergence. Strong emergence is defined when “truths concerning that (high-level) phenomenon are not deducible even in principle from truths in the low-level domain” (Clayton & Davies, p. 244). Weak emergence on the other hand occurs “when the high-level phenomenon arises from the low-level domain, but truths concerning that phenomenon are unexpected given the principles governing the low-level domain” (Clayton & Davies, p. 244). The keywords to be scrutinized here are deducibility and expectation.

Strong emergence means that, in principle, even with all the knowledge and information one has of the low-level components and their interconnections, the high-level properties of a system are impossible to be deduced (at least within the limit of human cognitive functions). An example of a phenomena that is said to be strongly emergent, according to some philosophers, is the mystery of consciousness. ³

Weak emergence, on the other hand, means that given the knowledge and information of the low-level components and their interconnections, the high-level properties of a system could, in principle, be deducible but are unexpected. The task of this thesis, however, is not to dissect each of the subtle and specific interpretations of the concept of emergence. This thesis will concern itself only with the phenomena that are weakly emergent. As such, an emergent phenomenon will be defined as:
 
a distinct and new high-level property, such as behavior or function, emerges from the self-organizing aggregation and interaction of the lower-level components.

For the purpose of this thesis, I will use the term “high-level” (HL) to mean something as complex or abstract and the term “low-level” (LL) will be used to mean something as simple or rudimentary. And the general definition (as outlined above) is sufficient in describing the underlying principle of the concept that will apply to the domain of human-centered design. Examples of phenomena that are said to be weakly emergent are ubiquitous in visual design, such as complex visual patterns that are reducible and could be traced back to their individual elements. A more detailed look on emergence in visual design will be covered in the next section.

Emergence in Visual Design
In human visual perception, the study of Gestalt psychology played an important role in the principle of perceptual organization. While Gestalt does not exactly translate into English, in general it means “whole,” “configuration”, or “form” (Wallschlaeger & Busic-Snyder, 1992, p.337). Gestalt psychology bears a striking similarity to the theory of emergence. Wallschlaeger and Busic-Snyder (1992) premised that in Gestalt theory, it is necessary to “consider more than just the separate elements that make up an experience, because the total effects of a visual experience is different from the effect of the accumulation of all the separate parts [italics mine]” (p.338).

The interaction between the perceptual organization of the human mind and the physical organization of the world’s objects is what created a functional whole. As Koffka (1932) pointed out, “the looks of things are determined by the field organization to which the proximal stimulus distribution gives rise” (p.106). The example this section is going to investigate relates to the properties of form in visual perception, such as figure/ground relationship (positive/negative space), pattern, associations, and so on.

According to John Gero (1996), “A property that is only implicit, i.e., not represented explicitly, is said to be an emergent property if it can be made explicit.” That means the pattern configuration is only emergent if the configuration shape differs from the original element shape (Gero, 1996). In Figure 3.4, both the elements and the pattern configuration are explicitly represented as square. Therefore, they are not an emergent system. However, in Figure 3.5, the explicit tetris-like shape is formed out of the implicit shape of the square. Therefore, an explicit emergent form is represented.

 

Figure 3.4. Both the elements and the pattern configuration are explicitly represented.

 
 

Figure 3.5. The pattern configuration (tetris-shape) is made explicit from the implicit elements (square).

Visual emergence in the example shown below (Figure 3.6) adds another level of complexity. The arrangement of the basic element resulted not only in a new, more complex shape, but the extra-layer of the “phantom forms,” i.e., the negative spaces, also emerges from the configurations (Gero, 1996). Figure 3.7 shows a more complex system emerges by following certain rules. This orderly emergence could only be actualized when there are underlying internal properties of the basic component itself (innate structure) and the way the basic components are arranged — that is, the LL behaviors. The reason why rules are important is because of the consequence of intelligibility of the HL structure. If the LL rules are not well-designed, then the consequence is going to be unintelligible and chaotic (Figure 3.8).

 
 

Figure 3.6. Phantom form emerged from the configuration of the basic elements.

 
 

Figure 3.7. Established rules create a novel high-level complex system from low-level interactions.

 
 

Figure 3.8. When there’s no established rules, the result is often unintelligible.

In design pedagogy, rule-based method is also used for creative emergence. Paul Rand, one of the most influential graphic designers in the 20th century, pointed out that “a problem with defined limits, with an implied or stated discipline (system of rules), that in turn is conducive to the instinct of play, will most likely yield an interested student and, very often, a meaningful and novel solution” (Kepes, 1965, p.156).

The rule acts as constraints to foster student’s creativity. One example is illustrated in Tatjana LeBlanc’s “Sketching as a Thinking Process,” in which she assigned students in her classroom to use basic and abstract elements and gradually transform them into more complex configurations. Students were encouraged to use different strategies, one of which is bi-polar concept as a general rule. From the general rule of bipolar concept, more specific ones could be generated, for example, “singular to multiple,” “geometric to organic,” “orderly to chaotic,” and so forth. Out of that exercise, a more “methodical thinking” process emerged. LeBlanc used Gestalt psychology as the fundamental inspiration for the exercise she conducted in the classroom (LeBlanc, 2015).

Evidence in Natural and Social Systems
The phenomenon of emergence is ubiquitous in both the natural world and society. Let us first use water as another example. Water has the physical property of liquidity. If we look deeper, we could see that water is made up of the aggregation of two-parts Hydrogen molecules and one-part Oxygen molecule (otherwise known as H2O) of a certain configuration. We could say that the liquidity of water is a HL property that emerges from LL components that interconnect in a particular way. If those molecules were to be arranged in a different way, then the water will solidify and become ice. The solidity of water (ice) is a HL property that emerges from LL components that interconnect in a lattice structure. The different states of water correspond to the different interconnections of the molecular structure. This is an emergent phenomenon as the HL properties of liquidity and solidity could not be found on the properties of the LL components (Searle, 1992, p. 14). The LL interaction is a self-organization activity that stems from the bottom level rather than being directed by controls from the top. Depending on the context, LL components and their interactions could be embodied with different models and forms, such as rules.

For example, Steven Johnson (2001) outlines five rules of how LL motives could produce HL behavior:
1. “More is different” because emergence is complex and nonlinear by nature, it needs a substantial quantity of parts.
2. “Ignorance is useful.” The individual part needs to be simple and somewhat “unintelligent” and let collective behaviors emerge from the ground up.
3. “Encourage random encounters.” Rely on the quantity of parts, letting random encounters happen, as it is a feature of emergence.
4. “Look for patterns in the signs.” Being able to decode signs in the environment in order to let “meta information circulate” through the group.
5. “Pay attention to your neighbor,” where “local information can lead to global wisdom.” The individual part has to have certain interaction with the surrounding neighbors in order to generate more interaction (p.76–79).

Those rules provide explanations on how the ant colony works. There is a myth about the ant colony that the ant queen manages and controls the entire colony. A closer study of the behaviors of ants shows the contrary. According to Deborah Gordon (1996), “a social insect colony operates without any central control; no one is in charge, and no colony member directs the behavior of another.” One of the important questions to ponder is that if each insect behaves individually and does not give command to others, then how does the colony work? This is one of the core question of the emergent system.

In social behavior, a complex system arises due to the interactions between the individual components. Insects are able to not only detect signals and messages, but they are also able to provide positive and negative feedback. These interactions are influencing the LL actions that contribute to the dynamics of the colony. From HL perspective, a complex system emerged from the individual components that act locally. In other words, the colony of ants is formed by each individual ant acting autonomously, following very simple tasks. Then, the consequence of those individual actions gives rise to a complex behavior of a colony (Gordon, 1996).

The rules of complex systems can be made general or specific. For example, Sull and Eisenhardt illustrated a few simple rules honeybees use in searching for a new site for their colony: “Dance longer for better sites,” “Follow the first dancer you bump into,” and “Head-butt scouts promoting other sites.” Those could be categorized as general rules. From the general rules, more specific ones could be assigned, for example the type of dance can be expressed as a “figure-eight pattern (known as a waggle dance) on the back of her sister bees in the cluster” (Sull and Eisenhardt, 2015, pg.40). Rule-based methods are very powerful tools and they work incredibly well for complex systems.

Not only do rules appear in the social interactions of animal colonies, but they also govern human lives in society. Road intersections, for example, use a set of rules that help organize the traffic. Even though there will always be some level of formal controls, such as laws and regulations in place to reinforce those rules, in an emergent system, individuals self-organize (i.e., obey the rules) without explicit leaders or hierarchy.

The Amazon.com book recommendations system, grassroots political movements (such as Occupy movements), Wikipedia, are a few examples of self-regulated systems following certain rules. To understand the structure of such collective actions, scientists and researchers often use artificial environments such as computer simulations as the means to construct various models that would explain group behavior phenomena. The simulation of agents and their interactions is mostly known as Agent-Based Modeling, which is usually conducted in order to “understand properties of complex social system through the analysis of the simulation” (Axelrod, 1997, p. 3). One of the most important aspects of Agent-Based Modeling is based on Game Theory, which will be covered in the next section.

Game Theory & Agent-Based Modeling
Game Theory considers strategy as the main component of thought experiments. Prisoner’s Dilemma (Figure 3.9) is one classic example of game theory, where two players — criminals, for example — are interrogated in separate rooms for them to confess to their crimes. Each of them has the choice to either confess the crime to the interrogator (therefore defect from other player) or deny the crime to the interrogator (therefore cooperate with other player) with different payoffs. An equilibrium is reached when both players choose the optimal responses to the strategy of the other player. If both players choose to defect, then they each will get 6 years in prison. If both choose to cooperate, then the players will only get 3 years each. From this point of view, it seems that cooperation from both players is the soundest solution, because both players get the lightest sentence (3 years).

However, game theory assumes that players are rational agents, which means individuals make choices based on self-interest. Thus, in the prisoner’s dilemma, there will always be risks involved for both players when they cooperate, i.e., being the sucker when the other player decides to defect. If Player I chooses to cooperate, but Player II chooses to defect, then Player I will suffer a worse outcome (10 years in prison), than if he/she decides to always defect (6 years in prison). Thus, the equilibrium is reached when both players choose to defect. Even though the cooperate/cooperate strategy (3,3) gives the best outcome (but both players risked being the suckers, if the other player chose to defect), the defect/defect strategy is the one that (6,6) reaches stability.

 

Figure 3.9. Prisoner’s dilemma game, in which an equilibrium (highlighted) is reached when both players defect.

Agent-Based Modeling (ABM) is one of the research methods used in complexity theory. ABM uses Game Theory as the basic framework for the model to do thought experiments. According to Robert Axelrod, the purpose of ABM is to “aid intuition” — to analyze results by studying the different parameters of the rules and features of the components. ABM uses computer simulations and mathematical models to see how different artificial agents interact in a given condition. ABM is utilized in different fields of study, such as economics, social science and biology. Because many of those domains have many different variables and have highly complex interactions, it is necessary to assume simple rules. Running simulations of a certain system using simple rules may give rise to unexpected (and sometimes novel) behavior. The unexpected behavior could be said as an emergent property of the system (Axelrod, 1997).

The strategy outlined above (Figure 3.9) is an example of a one-off game — a game that is played only once. In iterative games (played many times), however, players have to consider future consequences. If one player chooses to cheat on one instance, there may be repercussion to the player’s choice on the subsequent instances — for example, retaliation from the other player.

A classic iterative prisoner’s dilemma game is demonstrated by Axelrod from a competition he held, where people (mathematicians, economists, sociologists, psychologists, etc.) were invited to submit computer programs, each with their own specific strategies for playing a prisoner’s dilemma game. The games were played repeatedly for 200 times. The purpose of the competition was to find out the best strategy when different programs (submitted by the competitors) were pitted against one another. Of the 14 participants, the winner was Anatol Rapoport, a Russian-born game theorist, in which he submitted a very simple strategy called Tit-for-Tat (TFT) (Komorita and Parks, 1994, p. 36–37).

TFT is basically a strategy that mirrors the other player’s actions, i.e., player responds with cooperation when the other cooperates, and player responds with defection when the other defects. The beauty of TFT’s simplicity lies in its balance between extreme cooperative and extreme competitive behavior. The player that adopts an unconditional cooperative strategy, i.e., no matter what the other player does, always cooperate, will very likely to be exploited. The player that is unconditionally competitive, will very likely be met with defection and poor payoffs as well. TFT strategy retaliates to defection from the other player to prevent being the sucker. Thus, it cannot be exploited. TFT is also “forgiving”, in that it will return to cooperative behavior, if the other player reverts back to cooperation after defection. TFT also always starts with cooperative behavior, and then reciprocates or retaliates depending on the other player’s choice thereafter. Thus, it is considered a “nice” strategy based on reciprocity. The simplicity and clarity of TFT also make the strategy easily understood (Komorita and Parks, 1994, p. 37).

In a complex and dynamic environment, it is very difficult to analyze and calculate the best strategy. Instead, players use trial and error to approach what strategy works and what does not. Axelrod used adaptation from the theory of evolution as an analogy to illustrate how successful strategies spread in the population and survive overtime. In fact, in the computer laboratory, the process of evolution from biology, called genetic algorithm, was adopted to conduct the simulated experiments. According to Axelrod, the genetic algorithm model was “inspired by the ability of evolution to discover adaptive solutions to hard problems” (Axelrod, 1997, p. 10).

Emergent Systems and Human-Centered Design
Unlike the unthinking artificial agents in computer simulations that are usually embedded with limited properties (i.e., features and attributes), people’s innate capacities are exponentially more complex. Studying artificial agents using ABM could shed light to the researchers on the fundamental features of social interactions in the real world. From the perspective of human-centered design, incorporating people’s intentions and creative capabilities with models such as ABM or using a rule-based method, is generating possibilities that have not been explored before. The synthesis between human-centered design and emergent systems also reframe the way problems are approached. Instead of designing to solve any particular and isolated problems, the paradigm shifts to designing for adapting to dynamic environments. The approach of this thesis is also proposing not only the collaboration of various disciplines, such as design, social science, philosophy, engineering, etc., but blurring the boundaries of those disciplines as well. Thus, this approach is neither making the design disciplines taking a “backseat” in beautification of any artifacts, nor is it framing the project focus as a design problem. Rather, this approach frames the project as a human problem.

As illustrated in the general concept of human-centered design, the core philosophy of human-centeredness pre-supposes that all humans are creative and principles of co-design are inherently inclusive so that people become active participants in the process. Because of the capabilities and potentials of collective creativity, rather than artifacts or rigid systems, it is people who become the integral parts of the solution.

Summary of Chapter 3
An emergent phenomenon occurs when a distinct and new high-level property, such as behavior or function, emerges from the self-organizing aggregation and interaction of the lower-level components. Emergent phenomena are ubiquitous in natural and social systems, e.g., the compositions of water molecules and the organization of the ant colony. Much has been explored and studied in the sciences and philosophy on the topic of emergence. While emergence had not been traditionally studied predominantly in the design disciplines, emergent systems are highly relevant to human-centered design, particularly in the field of participatory design (e.g., Sanders and Stappers, 2014). In exploring the emergence aspect in human-centered design, this thesis research not only investigated the rules of complex systems (e.g., Sull and Eisenhardt, 2015), but it also looked at game theory and agent-based modeling (e.g., Axelrod, 1997; Hechter, 1987; Komorita and Parks, 1994) that use artificial agents that contrast with real agents (i.e., human beings) in participatory design research. Chapter 4 will cover the application of the theories outlined in this chapter by going through different exploratory studies.

 

Notes
1. Some may argue that task-oriented research (a research method in which users perform specific tasks on a particular design concept and the performance is measured to see the effectiveness of the design) is a form of participatory design, which may be the case. However, in my view, the participatory design in the instance of task-oriented research is usually quite passive and this thesis does not concern itself with that passive form of research participation. Hence, I made the distinction between user-centeredness and human-centeredness to demarcate the passive-active dichotomy to simplify the discussion in this thesis. From here on, participatory design refers to the active form of research participation and the term is used interchangeably with co-design.

2. Direct democracy is self-governance by the people. Unlike indirect (or representative) democracy where people elect the decision-makers, in direct democracy, the people not only actually participate in the issues and decision-making, but the election of (short-term) officers uses the method of sortition (or allotment) in order to achieve fairness and equality. Aspects of this thesis topic’s bottom-up design approach (emergence system) are partly inspired by the principles of direct democracy. This thesis will not cover theories of democracy. However, I highly recommend Mogens H. Hansen’s historical analysis of democracy in ancient Greece called The Athenian Democracy in the Age of Demosthenes, to the readers who are interested.

3. A side note on the mystery of consciousness; To my knowledge, there seems to be no consensus amongst scientists, philosophers, and AI (artificial intelligence) researchers as to what philosophical problems (e.g., epistemic, ontological, dualism, and so on) consciousness falls under. Without going into many details (as there are so many fascinating discussions to the topic), to my understanding there are two main proposals regarding consciousness. On the one hand, consciousness is believed to be a mental state that IS NOT merely a physical property of the brain. This is a possibility proposed by David Chalmers. On the other hand, consciousness is to be believed as a mental state that IS purely a physical property of the brain. This view is proposed by John Searle. However, there seems to be little disagreement that “consciousness is a higher-level or emergent property of the brain” (Searle, 1992).

 
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