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Collective Intelligence in Generative Design: A Human-Centric Approach Towards Scientific Design

Collective Intelligence in Generative Design
A Human-Centric Approach Towards Scientific Design
Shervin Azadi
Department of Architectural Engineering and Technology
Delft University of Technology
Delft, the Netherlands
Pirouz Nourian
Department of Architectural Engineering and Technology
Delft University of Technology
Delft, the Netherlands
Mathematical formalization of knowledge within a scientific
paradigm unifies sporadic efforts through converging glossary
and notation, thus enabling scientists to identify knowledge
gaps and discrepancies easier. Furthermore, comprehensive
formalization reveals potential bridges to various domain
sciences and facilitates the utilization of methods that have
proven effective in scientific problem-solving. In the case of
Architecture and Built Environment, there is a long history
of scattered efforts for identifying and formalizing design
problems and design methodologies, but the big picture is
yet missing. In this short piece, we name and frame some
of these efforts to identify their parallels with Mathematics,
Computer Science, and Systems Theory, as well as to illustrate
new opportunities that methodical design unlocks.
In 1971, George Stiny and James Gips introduced ”Shape
Grammars,” which described a syntactical system for producing
geometrical configurations from a set of rules and one initial
axiom [1]. In their grammar, each rule specifies a geometric
transformation by illustrating the initial state (if) on the left
side and a final state (then) on the right side. Shape Grammars
is reminiscent of the Lindenmayer-System (L-System), which
was developed by the biologist Aristid Lindenmayer in 1968
to model the morphology of plants [2]. Both of these formal
grammars were focusing on encoding the process of geometric
transformation through a grammatical ruleset. Still, they diverge
in notation as L-System adopts a string-based notation to
describe each transformation while Shape Grammar has moved
towards a visual notation.
Similarly, in his 1977 book A Pattern Language, Alexander
describes an architectural system that consists of a set of local
rules in various scales of architectural design. Alexander’s
pattern language has inspired other engineering fields on
how to encapsulate evidence-based tacit knowledge in system
design as well [3]. In the same era, other approaches that
adopted the analogy of architectural configuration design with
linguistics and graph theory emerged, namely in the avant-
garde books of March and Steadman’s ’Geometry of the
Environment’ [4], ’Architectural Morphology’ of Steadman [5],
This is the author version of a paper with the same title and content published
in the BouT Rumoer 76:Generative Design pp.7-16
and Hillier and Hanson’s ’Social Logic of Space’ [6] that later
sparked the umbrella term Space Syntax. The latter especially
established the use of the terms syntax and morphology in
an obvious reference to linguistics. What is common between
their approaches is a view of architectural configuration as
a matter of graph construction. In addition to these, Yona
Fridman is arguably the first author to call for a ’scientific
and participatory’ approach to architectural configuration based
on graph theory in his inspiring book ’Towards a Scientific
Architecture’ [7]. In retrospect, all of these approaches can be
seen to have been inspired by the influential work of Noam
Chomsky on Generative Grammars [8].
Fig. 1: The Generator Project. source: CCA online archive [9]
Inspired by cyberneticians such as Gordon Pask and Norbert
Wiener, in 1976, Cedric Price and John Frazer formulated a
system theoretical framework for a generative architectural
configurator called the Generator Project [11]. The design was
configured by assigning locations to a set of 150 mobile cubes
(spatial units) and combining them based on connection rules.
In multiple ways, this generator was much ahead of its time by
defining a discrete notion of space and addressing configuration
and shape problems in a single framework. The Nobel laureate
Herbert Alexander Simon eloquently explains the importance
of a solid notion of [discrete] space in his famous book the
Fig. 2: The Generator Project: top-left, relation chart of user
acitivities inside a residential unit; top-right: Layout, source: MOMA
online archive [10]; bottom: Diagram of the system of relations
between factors; source: CCA online archive [9]
Sciences of the Artificial: ”Since much of design, particularly
architectural and engineering design is concerned with objects
or arrangements in real Euclidean two-dimensional or three-
dimensional space, the representation of space and of things in
space will necessarily be a central topic in a science of design”
A set of common threads are traceable through all of
these innovative perspectives on design. The foremost is
the analogy of architecture to language, which seeks to
distinguish morphology and syntax respectively for the study
of architectural forms and configurations and grammatical
rulesets for systematically defined architectural schools such
as classic architecture. The second is the notion of space
that lays a foundation for formalizing architectural design
as a matter of spatial configuration or formation of spatial
boundaries, whether through discretization of space as a grid
or modeling spatial relations as a graph. The great advantage
of such configurative approaches to design is a paradigm shift
from design as a matter of drawing toward design as a matter
of decision-making. This crucial thread is explicitly present in
the Generator Project’s diagram of the design process, which is
depicted as a data flow diagram (see Figure 2). These threads
are not independent of each other; a discrete model of space
empowers discrete spatial decision-making (e.g., in the form
of location-allocation problems), generative grammars regulate
the configuration of modules in a discrete space, and the
combination of decision-making approach and grammatical
structures can modularize the design process. The crucial role
of these reciprocal relations will come to the surface as we
elaborate on the idea of methodical design.
Methodically addressing the societal challenges such as
shortage of housing, urban inequality, climate crisis, and
scarcity of resources within architectural & urban design
processes would reveal human/physical complexities of design
problems; the complexities as to which design problems have
been referred to as ill-defined [13] or even wicked problems
[14]. Due to these complexities, it is generally not an easy task
to devise a course of actions that could be guaranteed to reach a
single design objective, let alone multiple ones, especially when
there is not even a consensus among the involved actors as to
what the goals and their priorities should be. In other words,
in the presence of complex human decision-processes and
multi-faceted physical phenomena, the relation between design
Choices and Consequences becomes intricate and non-trivial
to model, thus demanding approaches that take socio-spatial
complexities for granted [15], [16]. Such complexity-driven
approaches to the study of socio-technical phenomena are
generally known as Generative Sciences, advocating the use of
network science, Agent-Based Models, Cellular Automata, and
in general, stochastic simulations of Multi-Agent Systems for
understanding such complex systems [17]. Such complexities
have arguably created a knowledge gap concerning ’evaluating
design decisions.’
Consequently, there is a common tendency to jump to
conclusions in design processes from the abstract desired
functionality of a design to its ultimate concrete form, referred
to as the ”Logical Leap in Design” [18]. As such, the main
objective of methodical design approaches is to bridge this
gap by firstly formulating the problem of design, breaking
it into smaller formerly-classified problems, and devising a
corresponding course of actions. Subsequently, the methodical
design is necessarily tied to a systematic study of design
problems’ underlying complexities (i.e., multi-dimensional,
multi-criteria, multi-actor, and multi-value complexities illus-
trated in Figure 3). Once a design problem is understood in
such a non-reductionist form, it is easy to see the need for
(and a current lack of) comprehensive evaluation frameworks
capable of encoding, collating, and aggregating domain-specific
human/physical knowledge of design quality, e.g., the study of
spatial quality as to affordance, ergonomics, and daylight.
Generative Design in a broad architectural sense is an
umbrella term referring to the science of understanding and
converting the problem of architectural design to sequences
of decision problems, and devising Generative Systems for
solving these problems through (q.v. [19] and [20]):
mathematically deriving designs from given design re-
quirements (e.g. in graph-theoretical architectural layout
Fig. 3: the spectrum of complexities involved in built environment
design problems
planning [5], topology optimization [21] or shape opti-
mization [22]),
itemising design alternatives through graph grammars (e.g.
in [23]–[28]))
devising and collectively playing a game with multiple
human players to interactively explore choices and con-
sequences in a structured and regulated design process
(e.g. in consensual decision-making in multi-actor design
problems [29], collaborative gamified design [30], and in
student’s project in Figure 5, 6)
See a spectrum of generative design approaches in Figure 4. In
a broader scope, the primary focus of both generative design
and Generative Sciences is on understanding and managing the
non-trivial sequences of choices and their consequences through
simulating the dynamics of the underlying phenomena, agents,
and their interactions by devising Generative Systems. Epstein
emphasizes the explanatory potentials of generative systems
as they enable us to artificially simulate the proposed model
of a hypothesis and evaluate the similarity of the emergent
pattern with the natural one [17]. Ergo, simulation is the
critical ingredient of generative approaches as it provides a
comprehensive and reproducible understanding of the modelled
phenomena that effectively map choices to consequences. In
this sense, the scope of generative simulations goes beyond
the physical to include human factors for understanding the
human-induced complexities of socio-technical systems such
as negotiation dynamics, decision-making processes, subjective
biases, and bounded rationality.
Fig. 4: the spectrum of collective intelligence for spatial design
Figure 4 illustrates the spectrum of technics to generate
designs varying Grammatical Itemization that involves users
as the main driving force, to Mathematical Derivation with
minimum reliance on user participation; in the middle of which
Gamified Exploration is posited as it allows human participants
to be the main players while including computational systems
to ensure a logical structure and provide objective evaluations of
design alternatives as scoring mechanisms. Such a participatory
and generative formulation of spatial design problems allows
for human and machine agents to interact directly in the
design process, hence fostering the emergence of collective
Piere Levy defines Collective Intelligence (CI) as a ”form
of universally distributed intelligence, constantly enhanced,
coordinated in real time, and resulting in the effective mobi-
lization of skills” [31]. Here we focus on a particular type of CI
that emerges from the collaboration of natural and artificially
intelligent agents. On the natural side, CI exposes the decision-
making processes to the participants’ tacit knowledge and
insight into societal values. On the artificial side, it exploits the
precision, objectivity, and robustness that machine intelligence
can bring to the analysis and evaluation processes. The core of
such a CI is a shared medium that facilitates communication
and allows coordination between all agents by providing an
interactive and enjoyable interface for humans from one side
and a logical framework for computational agents on the other
As emerging media dominating the entertainment market,
games can provide entertaining and immersive experiences
while unfolding the complexity of the relations of choices with
prior conditions and posterior consequences. Besides, through
their logical structure, games can fully integrate artificial agents
in their system for simulations that can unravel the conse-
quences of choices. As such, games can provide prominent
media for engaging participants with complex systems that
have emergent characteristics [32]. It is essential to notice that
simulation in a more general sense than physical simulations
would also mean replicating the decision-making dynamics in
games (including board games). The term ’simulation game’
as such refers not only to digital simulation games but also to
the games or multi-actor strategic games that have a complex
decision as to their object of focus [32].
Games can implement multi-actor play and multi-criteria
scoring mechanisms thus not only providing for the direct
inclusion of participants in decision making. Furthermore, by
discretizing and structuring the nature of design decisions,
design games also provide for tracking, recording, and studying
design decision dynamics. The benefits of structuring decision-
making processes as games are twofold: on the one hand, the
negotiation process finds a rational and transparent basis, and
on the other hand, the decision dynamics can be investigated
to extract conclusions in the form of design principles relat-
ing performance indicators to decision-variables. Introducing
methods for evaluating the quality of designs alongside the
direct inclusion of participants in decision-making will facilitate
their direct reflection on the evaluation results. As such, a
gamified CI can didactically expose the complex nature of non-
linear relations of decision variables with the performance
objectives as well as the human complexity of decision-
analysis as to different value systems and the plurality of
actors. These potentials indicate that gamification can push the
design process towards a knowledge-based complex decision-
making discourse that contributes to resolving conflicts of goals,
perspectives, and interests for reaching inclusive consensual
decisions. Consequently, design solutions made through this
framework are inherently explainable and reproducible by
referring to the series of decisions that participants took and the
set of evaluations and analyses that the machine has performed
along the process.
By explicitly modelling a design process as a complex
decision-making process, and thus introducing decision vari-
ables, the combinatorial nature of the generative design will
most likely result in a so-called combinatorial explosion of
possible outcomes. Thus, the process of synthesis, i.e. exploring
large decision spaces, collating, and drawing a conclusion
from multiple analyses, can be overwhelming for humans and
demanding for systematic synthesis and search processes. In
this regard, algorithms and mathematical procedures can offer
Multi-Criteria-Decision-Analyses as well as non-linear Learn-
ing methods (typically categorized as Artificial Intelligence) to
perform the intricate task of relating consequences to choices
(design decisions) to guide such synthesis processes. However,
the adaptation and development of AI methods require a formal
definition of problems and methodologies that enable objective
evaluation, optimization, or adaptation of systems. Especially
in use-cases, where framing and formulating problems is
challenging due to the double human-physical complexity of
the concerned phenomena, any machine-generated solution
must be not only justifiable concerning a set of objectives
but also explainable [33] and interpretable [34] for humans
in terms of the clarity of the reasoning process. As design
problems typically have human-related complexities that lack
formal definitions, the interpretability of any method that leads
to a decision is essential for a CI system. Gamification of
design as a design-methodological approach offers mechanisms
for supporting ’direct and structured communication’ between
human agents and machine agents, required to foster CI [35],
making interpretability easily attainable.
The participatory generative approach to design as facilitated
by and structured in games reveals a non-reductionist picture
of the human-physical complexity of architectural design
processes. Transparently revealing such a complex picture and
relating design decisions to their performance consequences not
only makes design learnable as a knowledge-based process of
decision-making aimed at attaining high levels of performance,
but also an inclusive social decision-making process that
induces a sense of holistic responsibility towards measured
social and environmental consequences of long-lasting design
decisions. Generative Design Games can enable participants
to design effectively and intelligently while respecting societal
values and caring for the planet. Participatory Generative
Design in Architectural Design is an interdisciplinary field of
research that renders a growing list of questions/problems and
answers/solutions. The Laboratory of Generative Systems and
Sciences in Architecture and Built Environment GenesisLab,
is an open-science initiative for research, development, and
education in this emerging domain; seeking to contribute
to fostering new types of open collective intelligence for
responsible architectural design and holistic analysis of the
built environment.
Fig. 5: Examples of gamified generative design in student projects:
MSc Earthy Design Studio [36], [37]. Image Credits: TerraTetris by
Aditya Soman, Vicente Blanes, Christina Koukelli, Neha Gupta, and
Dion van Vlarken; Modulabity by Alessandro Passoni, Alessio
Vigorito, Fredy Fortich, Kiana Mousavi, and Stephanie Moumdjian
Authors Shervin Azadi and Pirouz Nourian were partially
supported by two research grants while working on the content
of this article: project EquiCity, Granted by Netherlands Organ-
isation for Scientific Research (NWO), the grant Idea Generator,
Nationale Wetenschapsagenda Nationale Wetenschapsagenda
and project GoDesign, Granted by the Dutch Ministry of
Education, Culture and Science (OCW), the grant Actieagenda,
Ontwerpkracht, Ontwerp en Overheid.
Fig. 6: Examples of gamified generative design in student projects:
BSc Spatial Computing Architectural Design Studio [38]. Image
Credits: CUB3D by Hugo van Rossum, Maren Hengelmolen, Liva
Sadovska, and Sander Bentvelsen
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This chapter addresses a specific type of game content, the dungeon, and a number of commonly used methods for generating such content. These methods are all “constructive”, meaning that they run in fixed (usually short) time, and do not evaluate their output in order to re-generate it. Most of these methods are also relatively simple to implement. And while dungeons, or dungeon-like environments, occur in a very large number of games, these methods can often be made to work for other types of content as well. We finish the chapter by talking about some constructive generation methods for Super Mario Bros. levels.
Explainability is essential for users to effectively understand, trust, and manage powerful artificial intelligence applications.
Agent-based computational modeling is changing the face of social science. In Generative Social Science, Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one "grows" the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such far-flung fields as archaeology, civil conflict, the evolution of norms, epidemiology, retirement economics, spatial games, and organizational adaptation. In elegant chapter preludes, he explains how these widely diverse modeling studies support his sweeping case for generative explanation. This book represents a powerful consolidation of Epstein's interdisciplinary research activities in the decade since the publication of his and Robert Axtell's landmark volume, Growing Artificial Societies. Beautifully illustrated, Generative Social Science includes a CD that contains animated movies of core model runs, and programs allowing users to easily change assumptions and explore models, making it an invaluable text for courses in modeling at all levels.