A paper* presented at EAD 2009 conference in Aberdeen, trying to explain why "design thinking" works and what may be its limits. This is an interesting line of research which may even produce some testable hypotheses in the future, and it has immediate practical implications for product development managers.
See also our slides on the subject!
* Korhonen, J. M. & Hassi, L. (2009). Design Thinking Unpacked: An Evolutionary Algorithm. In Proceedings of the Eight European Academy of Design International Conference, 261-265. Aberdeen. .
Design Theory - Lecture 02: Design processes & Problem solvingBas Leurs
This document provides an overview of design processes and problem solving. It discusses various models of design processes, including linear and iterative processes. It also examines the nature of design problems and how designers approach problem solving. The document highlights that design problems often have no single clear solution and require intuition and experimentation to develop potential concepts and solutions.
P bentley: aspects of evolutionary design by computersArchiLab 7
This document provides an overview of four main types of evolutionary design by computers: evolutionary design optimization, creative evolutionary design, evolutionary art, and evolutionary artificial life forms. It discusses each type in terms of definitions, examples of applications, typical representations and algorithms used. It also describes four overlapping types that combine elements of the main types. Finally, it discusses common problems faced in evolutionary design like interdependent elements, epistasis, and constraint handling.
This document discusses the concept design phase of the design process. It describes generating alternative concept designs by matching functional requirements to physical principles, materials, and geometries. Concepts are developed using techniques like morphological matrices and combining alternatives. Concepts are then analyzed using preliminary calculations and testing to screen for feasibility. Evaluation methods like Pugh's method and weighted rating are used to systematically evaluate the remaining feasible concepts and select the best concept designs.
Social Media 101 -Colleges Ontario-with-notesPatrick McKeown
This document provides an overview of social media and how it can be used effectively. It discusses the key benefits organizations can realize from social media, including deeper customer engagement, brand awareness, listening to customer needs, and managing customer support and crises. The document also covers popular social media platforms like Facebook, Twitter, YouTube and Flickr, and how each can be used for different business purposes like marketing, sales and customer service. It provides tips on getting started with social media and engaging audiences.
This document summarizes the environmental footprint of the average American over a 77-year lifetime. It details the massive quantities of resources consumed, such as nearly 20,000 eggs eaten, over 43,000 soda cans drank, and 627,000 miles driven. The document emphasizes how individual lifestyles accumulate to significant impacts, such as the need for 57 hens to produce one person's lifetime of eggs and using enough plastic to equal 715 pounds for diapers. It encourages reflection on how human lives collectively affect the planet.
A practical decision framework for sourcing product development servicesJ. M. Korhonen
My M.Sc. (Tech.) thesis that develops a decision framework and a checklist for sourcing product development activities from external consultants.
It's long and somewhat academic, but if you're interested in hearing how you should manage external product development relationships it may be worth reading, or asking me about it :).
Design Theory - Lecture 02: Design processes & Problem solvingBas Leurs
This document provides an overview of design processes and problem solving. It discusses various models of design processes, including linear and iterative processes. It also examines the nature of design problems and how designers approach problem solving. The document highlights that design problems often have no single clear solution and require intuition and experimentation to develop potential concepts and solutions.
P bentley: aspects of evolutionary design by computersArchiLab 7
This document provides an overview of four main types of evolutionary design by computers: evolutionary design optimization, creative evolutionary design, evolutionary art, and evolutionary artificial life forms. It discusses each type in terms of definitions, examples of applications, typical representations and algorithms used. It also describes four overlapping types that combine elements of the main types. Finally, it discusses common problems faced in evolutionary design like interdependent elements, epistasis, and constraint handling.
This document discusses the concept design phase of the design process. It describes generating alternative concept designs by matching functional requirements to physical principles, materials, and geometries. Concepts are developed using techniques like morphological matrices and combining alternatives. Concepts are then analyzed using preliminary calculations and testing to screen for feasibility. Evaluation methods like Pugh's method and weighted rating are used to systematically evaluate the remaining feasible concepts and select the best concept designs.
Social Media 101 -Colleges Ontario-with-notesPatrick McKeown
This document provides an overview of social media and how it can be used effectively. It discusses the key benefits organizations can realize from social media, including deeper customer engagement, brand awareness, listening to customer needs, and managing customer support and crises. The document also covers popular social media platforms like Facebook, Twitter, YouTube and Flickr, and how each can be used for different business purposes like marketing, sales and customer service. It provides tips on getting started with social media and engaging audiences.
This document summarizes the environmental footprint of the average American over a 77-year lifetime. It details the massive quantities of resources consumed, such as nearly 20,000 eggs eaten, over 43,000 soda cans drank, and 627,000 miles driven. The document emphasizes how individual lifestyles accumulate to significant impacts, such as the need for 57 hens to produce one person's lifetime of eggs and using enough plastic to equal 715 pounds for diapers. It encourages reflection on how human lives collectively affect the planet.
A practical decision framework for sourcing product development servicesJ. M. Korhonen
My M.Sc. (Tech.) thesis that develops a decision framework and a checklist for sourcing product development activities from external consultants.
It's long and somewhat academic, but if you're interested in hearing how you should manage external product development relationships it may be worth reading, or asking me about it :).
A retrospective on design and process synthesis.pdfDr.Kiran Patil
This document discusses the impact of process systems engineering on chemical process design over the past 40 years. It notes that process design involves both technical and non-technical issues. Simulation, optimization, and process synthesis methods have helped with design but also notes that progress is linked to computer technology improvements. The document discusses how mathematical programming models helped formulate design problems but have limitations. It also discusses the need for innovation in design and issues when the design space is unknown or goals are unclear. Understanding the true goals of design and improving communication in design teams is also discussed as important factors in the design process.
This book is not finished. We’ve been developing it over the past few years. It began as a manilla folder with copies of different process models. We completed the first “book” version as part of a project undertaken for Elaine Coleman and Sun’s Virtual Center for Innovation. We present this version for educational purposes only. We have obtained no permissions to reproduce any of the models. Copyrights remain with their owners.
If you know of any models which are not featured in this book, please feel free to share them with us.
Everyone designs. The teacher arranging desks for a discussion. The entrepreneur planning a business. The team building a rocket.
Their results differ. So do their goals. So do the scales of their projects and the media they use. Even their actions appear quite different. What’s similar is that they are designing. What’s similar are the processes they follow.
Our processes determine the quality of our products. If we wish to improve our products, we must improve our processes; we must continually redesign not just our products but also the way we design. That’s why we study the design process. To know what we do and how we do it. To understand it and improve it. To become better designers.
Design of Financial Services and Products
Depends on
Flat Earth Policy - See: Friedman
The World is Flat
Terra Firma Economics.com and
Terra Policy - Pool Re - Swiss Re - Admin Re - Banking on Accord
The document discusses how design is adapting to new complex systems shaped by emerging technologies like biotechnology. It argues that design can help navigate unfamiliar spaces by generating more possibilities through an exploratory process. The document also discusses how design can influence research by manipulating interdependencies to explore the fitness landscape and increase adaptability to changing topographies.
The document discusses engineering design and creativity. It begins by defining engineering design as a process of devising systems to meet needs, applying science and optimizing resources. It emphasizes that design involves establishing objectives, synthesis, analysis and evaluation. The document then discusses differentiating science, technology and engineering, with science concerning natural phenomena, technology modifying nature, and engineering applying technology for human purposes. It provides examples to illustrate the relationships between science, technology and engineering. The document also discusses characteristics of design such as objectives, constraints, functions and form. It describes using objective trees to clarify and organize design goals and subgoals. Finally, it discusses initiating creative designs and improving creativity, noting creativity involves intuition and sensing incomplete ideas that are later clarified
Resource Allocation Using Metaheuristic Searchcsandit
This document discusses using metaheuristic search techniques to solve resource allocation and scheduling problems that are common in software development projects. It evaluates the performance of three algorithms - simulated annealing, tabu search, and genetic algorithms - on test problems representative of resource constrained project scheduling problems (RCPSP). The experimental results found that all three metaheuristics can solve such problems effectively, with genetic algorithms performing slightly better overall than the other two techniques.
Design science, systems thinking and ontologies summary-upward a-v1.0Antony Upward
The document discusses designing an ontology for strongly sustainable business models using a systems approach and design science methodology. It proposes:
1) Setting clear objectives to minimize bias and gain feedback for improvement.
2) Iteratively building the ontology by examining its fundamental building blocks - function, structure, process, and context - through inquiry.
3) Evaluating the ontology using a diversity of knowledge sources to triangulate different worldviews for a well-rounded assessment.
1. Scenario based design uses narratives or stories to describe how users will interact with a system. These scenarios help designers understand user needs and how people will accomplish tasks with the system.
2. Scenarios are both concrete, providing specific examples of usage, and flexible, allowing for refinement and elaboration. This helps designers manage the fluid nature of design situations.
3. Considering scenarios promotes a work-oriented design process focused on the needs of users. Scenarios also help designers reflect on and evaluate their work throughout the design process.
This document discusses applying innovative models and theories to project management. It describes four main innovative concepts: TRIZ, morphological analysis, system concept-knowledge theory, and the method of focal objects. TRIZ involves analyzing patents to identify common inventive principles to solve problems and eliminate conflicts. Morphological analysis works backwards from outcomes to components rather than vice versa. The document argues that using these innovative models in project modeling software could provide more solution options to meet client objectives compared to current practices.
This document provides an overview of different models and perspectives on the design process. It discusses models that view design as oscillating between analysis and synthesis, with the analysis phase involving decomposing a problem and the synthesis phase involving recombining solutions. The document also notes that processes can be viewed as either converging then diverging or vice versa. It presents several models that depict design as an iterative process of alternating between divergence, where many options are considered, and convergence, where the options are narrowed down. The goal is typically to converge on a final design solution through this iterative process.
This document analyzes and compares two user-centered innovation strategies - design thinking and lean startup. Both strategies involve customers in the development process, though they differ in goals, approaches, and methods. Design thinking aims to generate general innovations by solving "wicked problems" through qualitative user research and iterative prototyping. Lean startup focuses on high-tech startups and emphasizes quantitative customer validation and pivoting ideas based on metrics to efficiently test hypotheses about what users want. While the strategies have similarities in methodology and process, this document provides a structured comparison to better understand their differences and similarities.
The Nature of Design Practice and Implications for Interaction Design Researc...Fran Maciel
This paper examines the relationship between design practice and interaction design research. It argues that interaction design research aimed at supporting practice has not always been successful because it has not been grounded in an understanding of design practice. The paper compares the nature of complexity in science and design, arguing that approaches from science may not be suitable for design due to fundamental differences in complexity. It concludes that interaction design research needs a deep understanding of design practice in order to develop approaches that are useful for practitioners.
Contemporary Software Engineering Practices Together With EnterpriseKenan Sevindik
The document discusses various software engineering concepts and technologies. It covers topics like prototyping, refactoring, piecemeal growth vs big bang development, agile manifesto principles, design patterns, test driven development, object oriented principles, aspect oriented programming, evolution of enterprise Java technologies like Spring and Hibernate frameworks. It provides recommendations for books related to these topics.
Ballard et al. 2016 - Filmmaking and Construction_ Two Project Production Sy...Christin Egebjerg
This document compares the project production systems of filmmaking and construction. It argues that filmmaking and lean construction share similarities in their commercial terms/contracting, organizational culture, and operating/management systems. Contracts in filmmaking and lean construction are awarded based on qualifications rather than price, and risk is borne by entities best able to manage that risk. Both have collaborative cultures with high team identification to project objectives, in contrast to traditional construction's command and control culture. Their operating systems also emphasize management by means and results rather than just results. The document proposes that lean construction's future state of integrated project delivery already exists in filmmaking.
Philosophy and Introduction of Engineering Design Nt Arvind
Detail introduction and Philosophy of engineering design concepts
while designing a new marketing based or feasibility product we have to look a proper understanding about the concepts of engineering design concepts
Reconceptualising Design Research for Design Seeking and Scaling. Short position paper by Cook and Bannan, June 2013. **Critical comment and pointers to related literature invited** Contact: john2.cook@uwe.ac.uk
An application of genetic algorithms to time cost-quality trade-off in constr...Alexander Decker
This document summarizes a research paper that develops an optimization model using genetic algorithms to solve the time-cost-quality trade-off problem in construction projects. The model aims to find the minimum cost for a construction project to meet certain quality levels within a given time limit. It does this by considering different activity execution modes and using genetic algorithms to efficiently explore the large solution space. The document provides background on optimization problems and techniques, an overview of the time-cost-quality trade-off problem and prior related research, and describes the objectives and approach of the developed genetic algorithms model.
Ralph and-wand-a-proposal-for-a-formal-definition-of-the-design-conceptSvetlana Postolachi
The document proposes a formal definition of the concept of design consisting of seven elements: agent, object, environment, goals, primitives, requirements, and constraints. It also proposes a conceptual model of design projects that views projects as temporal trajectories of work systems involving human agents who design systems for stakeholders using resources and tools. The definition and conceptual model are intended to provide clarity and structure for developing a cumulative research tradition on design.
Visual thinking colin_ware_lectures_2013_10_research methodsElsa von Licy
This document discusses research design and evaluation for empirical studies. It describes several goals of empirical research including testing theories, discovering how people interact with interfaces, comparing display methods, and measuring task performance. It then discusses non-empirical contributions like prototyping new interfaces and developing new evaluation methods. Several methods for evaluation are outlined, such as measuring errors and time on tasks, using rating scales, and conducting interviews. The spiral design method is presented for iteratively prototyping and evaluating a system. The document concludes with tips for study design like focusing on important targets and controlling variables.
Are shocks and crises good for strategic innovation, and what is the role of complexity of strategy, competitive intensity, and crisis details to strategic resilience and innovation? A paper presentation in DRUID 2012 conference in Copenhagen, June 2012.
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This document discusses the impact of process systems engineering on chemical process design over the past 40 years. It notes that process design involves both technical and non-technical issues. Simulation, optimization, and process synthesis methods have helped with design but also notes that progress is linked to computer technology improvements. The document discusses how mathematical programming models helped formulate design problems but have limitations. It also discusses the need for innovation in design and issues when the design space is unknown or goals are unclear. Understanding the true goals of design and improving communication in design teams is also discussed as important factors in the design process.
This book is not finished. We’ve been developing it over the past few years. It began as a manilla folder with copies of different process models. We completed the first “book” version as part of a project undertaken for Elaine Coleman and Sun’s Virtual Center for Innovation. We present this version for educational purposes only. We have obtained no permissions to reproduce any of the models. Copyrights remain with their owners.
If you know of any models which are not featured in this book, please feel free to share them with us.
Everyone designs. The teacher arranging desks for a discussion. The entrepreneur planning a business. The team building a rocket.
Their results differ. So do their goals. So do the scales of their projects and the media they use. Even their actions appear quite different. What’s similar is that they are designing. What’s similar are the processes they follow.
Our processes determine the quality of our products. If we wish to improve our products, we must improve our processes; we must continually redesign not just our products but also the way we design. That’s why we study the design process. To know what we do and how we do it. To understand it and improve it. To become better designers.
Design of Financial Services and Products
Depends on
Flat Earth Policy - See: Friedman
The World is Flat
Terra Firma Economics.com and
Terra Policy - Pool Re - Swiss Re - Admin Re - Banking on Accord
The document discusses how design is adapting to new complex systems shaped by emerging technologies like biotechnology. It argues that design can help navigate unfamiliar spaces by generating more possibilities through an exploratory process. The document also discusses how design can influence research by manipulating interdependencies to explore the fitness landscape and increase adaptability to changing topographies.
The document discusses engineering design and creativity. It begins by defining engineering design as a process of devising systems to meet needs, applying science and optimizing resources. It emphasizes that design involves establishing objectives, synthesis, analysis and evaluation. The document then discusses differentiating science, technology and engineering, with science concerning natural phenomena, technology modifying nature, and engineering applying technology for human purposes. It provides examples to illustrate the relationships between science, technology and engineering. The document also discusses characteristics of design such as objectives, constraints, functions and form. It describes using objective trees to clarify and organize design goals and subgoals. Finally, it discusses initiating creative designs and improving creativity, noting creativity involves intuition and sensing incomplete ideas that are later clarified
Resource Allocation Using Metaheuristic Searchcsandit
This document discusses using metaheuristic search techniques to solve resource allocation and scheduling problems that are common in software development projects. It evaluates the performance of three algorithms - simulated annealing, tabu search, and genetic algorithms - on test problems representative of resource constrained project scheduling problems (RCPSP). The experimental results found that all three metaheuristics can solve such problems effectively, with genetic algorithms performing slightly better overall than the other two techniques.
Design science, systems thinking and ontologies summary-upward a-v1.0Antony Upward
The document discusses designing an ontology for strongly sustainable business models using a systems approach and design science methodology. It proposes:
1) Setting clear objectives to minimize bias and gain feedback for improvement.
2) Iteratively building the ontology by examining its fundamental building blocks - function, structure, process, and context - through inquiry.
3) Evaluating the ontology using a diversity of knowledge sources to triangulate different worldviews for a well-rounded assessment.
1. Scenario based design uses narratives or stories to describe how users will interact with a system. These scenarios help designers understand user needs and how people will accomplish tasks with the system.
2. Scenarios are both concrete, providing specific examples of usage, and flexible, allowing for refinement and elaboration. This helps designers manage the fluid nature of design situations.
3. Considering scenarios promotes a work-oriented design process focused on the needs of users. Scenarios also help designers reflect on and evaluate their work throughout the design process.
This document discusses applying innovative models and theories to project management. It describes four main innovative concepts: TRIZ, morphological analysis, system concept-knowledge theory, and the method of focal objects. TRIZ involves analyzing patents to identify common inventive principles to solve problems and eliminate conflicts. Morphological analysis works backwards from outcomes to components rather than vice versa. The document argues that using these innovative models in project modeling software could provide more solution options to meet client objectives compared to current practices.
This document provides an overview of different models and perspectives on the design process. It discusses models that view design as oscillating between analysis and synthesis, with the analysis phase involving decomposing a problem and the synthesis phase involving recombining solutions. The document also notes that processes can be viewed as either converging then diverging or vice versa. It presents several models that depict design as an iterative process of alternating between divergence, where many options are considered, and convergence, where the options are narrowed down. The goal is typically to converge on a final design solution through this iterative process.
This document analyzes and compares two user-centered innovation strategies - design thinking and lean startup. Both strategies involve customers in the development process, though they differ in goals, approaches, and methods. Design thinking aims to generate general innovations by solving "wicked problems" through qualitative user research and iterative prototyping. Lean startup focuses on high-tech startups and emphasizes quantitative customer validation and pivoting ideas based on metrics to efficiently test hypotheses about what users want. While the strategies have similarities in methodology and process, this document provides a structured comparison to better understand their differences and similarities.
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This paper examines the relationship between design practice and interaction design research. It argues that interaction design research aimed at supporting practice has not always been successful because it has not been grounded in an understanding of design practice. The paper compares the nature of complexity in science and design, arguing that approaches from science may not be suitable for design due to fundamental differences in complexity. It concludes that interaction design research needs a deep understanding of design practice in order to develop approaches that are useful for practitioners.
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This document compares the project production systems of filmmaking and construction. It argues that filmmaking and lean construction share similarities in their commercial terms/contracting, organizational culture, and operating/management systems. Contracts in filmmaking and lean construction are awarded based on qualifications rather than price, and risk is borne by entities best able to manage that risk. Both have collaborative cultures with high team identification to project objectives, in contrast to traditional construction's command and control culture. Their operating systems also emphasize management by means and results rather than just results. The document proposes that lean construction's future state of integrated project delivery already exists in filmmaking.
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* Korhonen, J. M. & Hassi, L. (2009). Design Thinking Unpacked: An Evolutionary Algorithm. In Proceedings of the Eight European Academy of Design International Conference, 261-265. Aberdeen, UK.
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Paper Link: https://eprint.iacr.org/2024/257
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Overview
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Design thinking unpacked: an evolutionary algorithm?
1. 8th European Academy Of Design Conference - 1st, 2nd & 3rd April 2009, The Robert Gordon University, Aberdeen, Scotland
DESIGN THINKING UNPACKED: AN EVOLUTIONARY ALGORITHM
Janne KORHONEN1, Lotta HASSI2
1
S.E.O.S. Design Ltd
2
Helsinki University of Technology
ABSTRACT
“Design thinking” has been proposed as a method for achieving better solutions in fields as diverse as
architecture and organizational design. Despite various definitions for design thinking, there have been
relatively few articles that aim to explain on a general level how and why, and under what preconditions,
design thinking actually works. This paper presents a hypothesis that design thinking can be understood
as a generic problem-solving algorithm whose qualities can be compared to other generic algorithms. The
similarities between design thinking and evolutionary optimization algorithms suggest that design
thinking is among the best possible algorithms for seeking solutions to ill-defined problems where there is
no single obvious “best” solution. This helps to better define what design thinking is and leads to practical
implications for product development managers and designers.
Keywords: design thinking, evolutionary optimization, evolutionary algorithm, problem solving, NPD
1 INTRODUCTION
Recent literature in design practices has highlighted the benefits of multidisciplinary product development
teams, human-centred exploration of user’s real needs, and early and iterative prototyping of ideas and
concepts. Often called “design thinking,” proponents of the method claim that resulting inductive-
deductive-abductive method works much better than previous, primarily deductive methods of R&D
problem-solving in fields ranging from education to engineering (e.g. Brown, 2008). However, the
benefits of “design thinking” approach are often unsupported by theoretical background. This study
makes an attempt to build a theory of real-world new product development (NPD) processes1 as problem-
solving algorithms, while comparing them to problem-solving algorithms whose effectiveness can be
theoretically examined. In effect, this study examines whether or not, and under what conditions, “design
thinking” is the most effective problem-solving tool available to NPD teams.
The approach used is to look NPD process through the lens of evolutionary optimization theory. Although
evolution has been often seen as a metaphor for design and particularly technological development (see
Ziman, 2000 for an overview and further references) and some authors have viewed design practices as
evolutionary (e.g. Langrish, 2004; Whyte, 2007), it can be argued that instead of directly mapping
biological into technical, the discussion should move to a higher level of generic algorithms. As an
example of more modern approach, Beinhocker (2006) has elegantly explained technology S-curves
(Foster, 1986) and disruptive innovations (Christensen, 1997) as results from evolutionary algorithm in
action. This study builds on Beinhocker’s work and attempts to explain NPD process as an example of
evolutionary algorithm, and develop practical implications for designers and managers.
The paper is organized as follows: first, product development is defined as a search for optimal design
from a hypothetical “design space.” Second, theoretical properties of design spaces are discussed. Third,
theoretical search strategies are briefly introduced and evaluated. Similarities between search strategies
and design approaches are discussed. Fourth and finally, practical implications are briefly outlined.
2 NEW PRODUCT DEVELOPMENT: A SEARCH THROUGH A DESIGN SPACE
Generalized models of product development process have been developed by numerous authors (see e.g.,
Brown and Eisenhardt, 1995; Ulrich and Eppinger, 2008), and what is clear is that NPD process seeks to
1
(Although this text refers to new product development for reasons of clarity, the principles apply to other
development projects as well.)1
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2. develop knowledge about technologies, markets, and users, and apply that knowledge towards more
effective and efficient products and services. Thus, product development is fundamentally about
searching for the “blueprints” of the best possible design. These blueprints can be thought to be housed in
a practically infinite library of blueprints for everything - to paraphrase Dennett (1995), the “Library of
All Possible Designs.” Within this library, blueprints for all the different combinations of all the parts,
materials, manufacturing processes and usage scenarios can be thought to exist, from stone axes to
spaceships. This library is referred in evolutionary theory as a design space for all possible designs
(Dennett, 1995, pp. 107-113). Design spaces can be constructed for anything that can, in principle at
least, be memorialized in some form and transmitted to others (Dennett, 1995; Beinhocker, 2006). Design
spaces can be constructed in smaller or larger variations: for example, a design space for combining two
Lego blocks with each other is relatively small, while a design space for all desktop computers is
enormous. As can be imagined, the huge majority of the entries in this library are outright impossible to
realize and/or completely useless; others are just tiny variations of existing - or future - designs. To
determine whether one design is better than some other, the design space is visualized as a fitness
landscape (Wright, 1932; Kauffman, 1993). By first arranging the design space so that the distance
between two designs represents the differences between the designs, and then assigning a value of fitness
for each design, we can imagine a landscape of peaks and valleys. Good designs can thus be thought of as
high peaks in the fitness landscape, and the problem of finding good designs in the near infinity of design
space then becomes a problem of finding high peaks in the fitness landscape. (Figure 1)
Figure 1 Examples of fitness landscapes
Fitness is defined here as fitness for purpose. A design is considered to have better fitness than another
design if it performs its intended function more efficiently or effectively. Fitness is a dynamic function,
and what is fit at one point of time may not be fit some time later. Typically, designs have a minimum
fitness that determines, for example, whether the design does what it should do. (Beinhocker, 2006).
Potentially, two extremes for the fitness landscape are possible: first, that there is no correlation between
the difference of two designs from each other and their fitness, resulting to a completely random
landscape (Fig. 1c). The second extreme is a perfectly ordered landscape where a single design is most fit
and the fitness of designs becomes lower as their distance from that optimum design increases (Fig. 1a).
In the real world, most fitness landscapes are somewhere in between (Kauffman, 1995, pp. 161-189) (Fig
1b). Two designs that differ in very small details - say, a PC with either grey or beige casing - are likely
to be similar in fitness. However, some small changes may have very large effects (for example, changing
the measurements from metric to Imperial is a very small change in terms of information changed), while
some large changes may only have a small effect (e.g. Intel and AMD processors are different internally
but work equally well). A design space in which small changes in design lead to small or no changes in
fitness, but where some small changes have large effects is called rough-correlated (Beinhocker, 2006).
Rough-correlated landscapes are the standard in biology and in most technologies, and the result is
important because what is the most efficient algorithm for the search depends on the shape of the fitness
landscape.
3 SEARCHING THROUGH THE FITNESS LANDSCAPE: SEARCH ALGORITHMS
How, then, can this library of all possible designs be searched in a most efficient manner? In keeping with
the two extremes of fitness landscapes, two extreme search strategies can also be identified. The search
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3. strategy can be completely random, with previous iterations having no effect on future iterations. In
product development terms, this would amount to generating random patterns and building prototypes
from those patterns, and then discarding the pattern and trying again if the prototype didn’t work out. This
is obviously a high-risk strategy, which isn’t practiced deliberately in the real world.
The search can also be based on random mutation hill-climbing (Mitchell, 1996, p. 116) or adaptive walk
(Beinhocker, 2006). In adaptive walk, the searcher would take a step in a random direction in the fitness
landscape. If the step leads upwards, the searcher would take another step; if it didn’t, the searcher would
return and take another random step. This strategy is effective for scaling the individual peaks in the
fitness landscape. In product development terms, adaptive walk is analogous to perfecting - often
deductively - the existing technology via incremental improvements, but without looking for alternatives
in technologies or user needs. Examples of (mostly) adaptive walk strategies in real life are all R&D
projects based primarily on applying theory to find out what is possible and how, and only then realizing
the design - for example, increasing the packing ratio in integrated circuits. However, if the “landscape”
to be searched is rough-correlated, both extreme strategies are likely to lead to suboptimal results. Since
there are many more unworkable designs than good designs, the odds are that random search will not find
fitness peaks. On the other hand, adaptive walk may find a feasible design, but, unless there is an option
of retracing the steps and starting over again, it probably gets stuck in a sub-optimal fitness peak. Of
course, real-world NPD processes utilize several strategies simultaneously: humans should be able to use
their reasoning facilities to determine when they are stuck in a “fitness peak” and then move on to some
other avenue of approach that promises better results, i.e. higher peaks. In terms of fitness landscapes and
search strategies, this would amount to going back down, once a fitness peak for a particular approach has
been reached.
But going back may be difficult since many processes are path-dependent, meaning that choices made
during the process limit what options are - or are perceived to be - available later. Choices made early
during the process can, for example, determine the technologies used, and once a choice is made, it may
be difficult to question. Whether path dependency is real or just perceived, the end result is similar: once
a fitness peak is reached, NPD teams have to consciously wrest themselves to seek for other, possibly
higher, fitness peaks, just as firms have hard time switching technologies even though newer technology
promises better future returns (as elaborated by e.g. Christensen, 1997). This phenomenon is familiar to
many NPD professionals: changing major design features halfway through the process is difficult due to
the attachment team members have to something they’ve been working hard to create, even if the
proposed design is clearly underperforming. Market and business demands count, too: even if the
proposed design isn’t really what is needed, descending the fitness peak and trying to find another way up
may be impossible due to financial pressures and plain organizational inertia. Retracing the path is often
impossible because doing so would mean relinquishing investments in a design that is still somewhat
successful, even though it may be clear that a competing technology would be better in the long run.
4 DESIGN THINKING: THE BEST SEARCH STRATEGY FOR ROUGH-CORRELATED LANDSCAPES
However, once NPD is defined as a search for knowledge about technologies, users and markets through
rough-correlated fitness landscape of all possible designs, mathematical tools can be utilized to determine
the quality of search strategies. The best strategy turns out to be, in general terms, an evolutionary
algorithm (e.g. Kauffman, 1993; Mitchell, 1996). Evolutionary algorithms are a class of search algorithms
that combine adaptive walk and random search: evolutionary search goes uphill towards the fitness peak,
but also randomly tries other locations to see if they happen to harbor more promising peaks. (Figure 2.)
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4. Figure 2 Different search strategies. X1…n represent exploratory search, dotted lines exploitative search
Most innovation-related activities resemble evolutionary search algorithms to a greater or lesser degree.
By combining iterative tinkering and testing (inductive) with analysis (deductive) and sprinkling some
imagination (abductive thinking), they proceed through both radical (randomly testing other locations for
more promising ways uphill) and incremental (going uphill if there’s a way) improvement. Iterative
tinkering remains essential to the development of innovations, even as the advances in science and
technology have improved our capabilities for deduction: most complex design problems remain
deductively intractable and are likely to do so, but whether to use primarily inductive or primarily
deductive methods depends on the nature of the problem. Well-defined problems that have no ambiguities
can be solved through deductive analysis; ambiguous or ill-defined problems with no clear boundaries
need inductive (and abductive) thought and tinkering. (Figure 3.)
Figure 3 Examples of well- and ill-defined problem spaces
But how are design thinking and evolutionary methods connected? To understand the connection, we
need to go back to the definition of NPD as a knowledge-generating process. The goal of the process is to
generate knowledge that satisfies the needs of process stakeholders, with economic performance of the
resulting product being one of the most obvious fitness criteria. Traditionally, under different “waterfall”
and other non-integrated models of product development, fitness of knowledge thus produced is
ultimately tested when the product reaches the markets; under design thinking and other integrated,
human-centred, iterative approaches (Brown, 2008), the validity and fitness of knowledge is tested much
earlier in the process, when corrections can still be made. In terms of fitness landscapes and search
strategies, traditional (non-integrated) NPD models represent search strategies that have mostly adaptive
walk features. Because length of each “step” is rather long (as knowledge’s fitness is validated only very
close to market launch), the step is usually taken into a “safe,” known direction. There is probably at least
some iterative tinkering, but exploration is limited and “far-out” possibilities are rarely if ever explored.
On the other hand, iterative and creative multi-disciplinary approaches have much more random jumps
built into them. Deductive, exploitative search (often by engineers) is used to incrementally improve the
quality of knowledge and find the peaks of “fitness hills” scouted out by more exploratory search (often
by designers), resulting in evolutionary search through fitness landscape. Because the process is human-
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5. centered and rough prototyping is encouraged, knowledge’s fitness is often tested much earlier than in
other models; because the process focuses on user needs, sometimes the team may see other fitness hills
(e.g. alternative ways of solving the user’s problem) than just the one where they start.
5 PRACTICAL IMPLICATIONS
One of the most important implications is that the fitness landscape of a problem defines whether
evolutionary or adaptive search is more suited to the situation. In practical terms, this suggests a
provisional theoretical explanation why design thinking and other methods which are weighed towards
exploration rather than exploitation are good at ill-defined problems, but not cost-effective for problems
that have heavily ordered fitness landscapes i.e. that are solved primarily through deductive methods.
This result, if it can be confirmed by further study, would be an important qualification to the somewhat
nebulous concept of “design thinking” and helps to focus further studies on the subject. Second
implication, flowing directly from the first, is that if the fitness landscape of a NPD project is possible to
predict or estimate to some extent, it should be possible to decide whether the balance of methods used in
the NPD process should be on exploratory (inductive) or exploitative (deductive) methods.
Third important implication of the theory is the effect that the fitness landscape has on product
development project. Since many fitness landscapes represent steep hills, progress in a typical project is
initially slow, accelerates after a while, and then peaks out in an S-curve-like manner. When the curve
flattens out and a local fitness maximum is reached, the NPD project tends to lose momentum, even
though other fitness peaks might be worth exploring as well. This is, in effect, a theoretical explanation
for practices such as “dark horse” used at Stanford University’s E310 product development project: by
forcing the team to develop, for a while, a concept rejected early in the process (the “dark horse”), the
management ensures that at least one other fitness hill is explored. Practical implication for NPD
managers is that the point to guide the team towards alternative approaches is not only at the beginning of
the project, but also at the point where the S-curve of accomplishment evens out. Fourth, since different
fitness functions define their own design spaces, different parts of the project may benefit from different
search strategies. For example, technology for widgets may be developed through mainly exploitative
search, while business plan associated with selling the widgets may require more exploratory approach.
REFERENCES
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BROWN, S.L. and EISENHARDT, K.M. 1995. Product development: Past research, present findings, and future
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BROWN, T. 2008. Design Thinking. Harvard Business Review, June 2008, pp. 84-92.
CHRISTENSEN, C.M. 1997. The Innovator’s Dilemma. Boston: Harvard Business School Press.
DENNETT, D.C. 1995. Darwin’s Dangerous Idea. New York: Touchstone.
FOSTER, R. 1986. Innovation: The Attacker’s Advantage. New York: Summit Books.
KAUFFMAN, S. 1993. The Origins of Order. New York: Oxford University Press.
KAUFFMAN, S. 1995. At Home in the Universe. New York: Oxford University Press.
LANGRISH, J.Z. 2004. Darwinian Design: The Memetic Evolution of Design Ideas. Design Issues 20(4), pp. 4-19.
MITCHELL, M. 1996. An Introduction to Genetic Algorithms. Cambridge, MA: MIT Press.
ULRICH, K.T. and EPPINGER, S.D. 2008. Product Design and Development (4th Ed.). Boston: McGraw-Hill.
WHYTE, J. 2007. Evolutionary Theories and Design Practice. Design Issues 23(2), pp. 46-54.
WRIGHT, S. 1932. The roles of mutation, inbreeding, crossbreeding and selection in evolution. Proceedings of The
Sixth International Congress of Genetics, vol. 1, pp. 356-366. Ithaca, NY.
ZIMAN, J. (Ed.). 2000. Technological Innovation as an Evolutionary Process. Cambridge: Cambridge Univ. Press.
Corresponding Author Contact Information
1 2
Janne KORHONEN Lotta HASSI
S.E.O.S. Design, c/o Design Factory Helsinki Univ. of Tech., Design Factory
P.O. Box 7700, FI-02150 TKK, Finland P.O. Box 7700, FI-02150 TKK, Finland
janne.korhonen@seos.fi lotta.hassi@tkk.fi
+358 41 501 8481 +358 50 511 2962
www.seos.fi www.decode.tkk.fi
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