ELSI: Cross-Domain Effects Part 1

90-100 Plasterboard

1.2 40-60 Loam building board

-0.2 no data Timber boards

-26 50-90 Veneer plywood

-23 50-90 (Source: Energy Manual; Sustainable Architecture by Manfred Hegger, Matthias Fuchs, Martin Zeumer, and Tommy Stark) Example of a working document using ELSI to map an integrated value assessment of soil, to use in Natural Capital frameworks. (Jade Moors, Tom Bosschaert, 2014) An exploration like this can be quickly made using free VUE software, and be used as a brainstorm, exploration, or impact registration map. Example of a natural capital quickscan on the value of a tree using SiD’s indicator framework. Can you guess how many bird species exist? And how many mammals? What about plants? Are there more fish than fungi species? Here’s a short list, with a rough approximation of the known and expertly guessed number of species, collected from various sources. Kingdom Known/estimated species Bacteria 4.000 Protoctists (algae, protozoa) 80.000 Animals (vertebrates) 67.700 Of which Birds 9.000 Of which Mammals 6.500 Of which Fish 33.500 Of which Amphibians 8.000 Of which Reptiles 10.700 Animals (invertebrates) 1.272.000 Fungi 72.000 Plants 270.000 Total known species 1.750.000 Possible species (including unknown) 14.000.000 species count Using ELSI to explore the concept of ‘leisure’ in the context of a more livable city of Rotterdam (Except 2013) global health statistics, top causes of death example: healthy buildings pay for themselves We wanted to figure out what the return on investment of health and productivity measures for offices are. Except’s science team collected data in meta-research about all aspects that may influence worker productivity. The diagram on the right shows that in a typical office, over 80% of the costs in that office are employee wages, and only 10% is office cost and upkeep. Below, a table with performance increasing measures. In total, we estimate between 10% and 20% of productivity increase can be gained through health and wellbeing measures. This pays back the entire construction cost of a typical office in 10 years. Improvement item Productivity increase % notes and Suggestions Light 2,8 - 11,4 Natural light always better than artificial. Use wide-spectrum non-flickering LEDs, with min. 500 lux at the desk (pref. natural light), with personal control. Outdoor view

View towards substantial nature Vegetation 6 - 15 Use indoor plants, use of natural materials (i.e. stone, wood etc.) Simulated nature <1 If real nature is not possible, screens, pictures etc. have little positive impact Colors <1 Research is inconclusive about color use Air quality 4,4 - 7 Mechanical ventilation with fine particle filter (e.g. carbon active), use of materials which do not emit VOC, min 10 liters/second of air supply per occupant. Acoustics 5,4 Limit noise from outdoors, limit noise from mechanical systems, limit noise between working stations. Thermal comfort

Summer: 23,5 – 25,5 °C, Winter: 21,0 – 23,0 °C, homogeneous temp. distribution, personal control / feeling of being in control of temperature set point. Happiness score of countries according to World Happiness Report of Columbia University (2018) ELSI Rose case study: redefining health Re-Defining Health with SiD’s Systems Lens For years, health has been viewed as a mere ‘absence of disease’, which is not really informative when trying to look at what healthcare is supposed to mean and be. In line with SiD’s definition of sustainability, health can be seen as a state of a complex dynamic system. In this view, different internal and external ‘objects’ such as hormones, food intake, and exercise interact with each other on a network level, together producing some state of the ‘health’ system as a whole. Specifically, in order to be able to speak of health, the state of the system needs to be resilient, which means that one can recover fairly quickly and sustainably after some imbalance. The system also needs to be harmonious, which in the case of a non-societal system such as a human body is inherent (not attacking itself), as well as autonomous, meaning it needs to be supplied with the right amount of resources to operate properly (food, water, air, physical exercise, sunlight, etc), and can freely move to seek more optimal conditions if required. By stressing the need for resilience, and thus the ability to recover from an ailment, this definition recognizes that a person can experience physical or mental issues from time to time as part of “normal operations”. This, as opposed to the traditional view to cure each ailment separately, is therefore more helpful in light of current trends in demography and healthcare technology. Moreover, treatment can be directed at establishing (long-term) resilience and not at mere symptom reduction. So, this view both clarifies what health is, and contributes to more sustainable ways of treating health related issues. Applying SiD to (Un)healthy People In line with this definition of health, we use SiD as a starting point in working towards resilient physical, mental, and social systems. I’ll illustrate this by zooming in on mental health diagnosis and treatment. Research has increasingly shown that mental disorders are not fixed entities, but emerge from interactions between different biopsychosocial aspects. On top of that, the ‘objects’ and network structures making up these mental health systems differ across individuals. When taking the systemic nature of mental health into account, it makes little sense to diagnose people with certain ‘mental disorders’ and trying to fix these using a unified approach, which is currently the case. Using SiD-like methodologies to map the system helps to achieve better goals and better solutions. It goes without saying that SiD can be used in designing and improving other health related topics, such as cardiovascular problems related to lifestyle, or ‘vague’ chronic ailments that do not seem to have a simple cause . The resulting insights then consist of, for example, interrelations between mood, anxiety, genetic disposition, lifestyle factors, social environment, and protective factors like purpose and fulfillment, together making up a patient’s mental state. There are multiple ways of mapping this system, both qualitative and quantitative, which can function as an individualized diagnosis. Both bottom-up and top-down approaches of analyzing the system are then used to arrive at the right intervention to shift the state of the system of individual people towards a more resilient one. This can then include a variety of solutions in a ‘roadmap’, including medicinal, therapeutic, biophysical, psychological, behavioral and cultural aspects. If the mapped system is not too big, it’s possible to analyze the quantitative structure for most central objects that function as a leverage point in shifting its dynamics, and thus quickly find remedies. If it is too big to map, therapists and patients can use a top-down analysis approach such as SiD’s climbing-the-hill approach to start discovering solutions in the form of iterative interventions. Roadmaps are drawn to infer whether the effect of the interventions have the desired effect, and ‘the system’ is evaluated and updated along the road of treatment. Currently, in psychological science, studies are designed that investigate possibilities of implementing such an approach in mental healthcare, and results are expected to be published in the coming years. Expanding beyond ‘sustainability’ To conclude, SiD can be applied beyond ‘classic’ sustainability questions onto other complex constructs in which sustainability is a desired goal. SiD can be used in designing and improving other health related topics, such as health systems on the meso level, i.e., healthcare organizations, cultural patterns affecting health, and global health systems on a macro level. In this way, SiD enables us to see the complex world as it is and develop actionable interventions to all kinds of systems to make them truly sustainable. RAH system indicators System Level: Resilience Resilience is a system’s capacity to withstand (unexpected) external disturbances and its ability to return to a healthy state after suffering a blow. Resilience is not to be confused with toughness or strength. For example, for purposes of personal self-defence, techniques which rely on agility and flexibility such as Aikido are more effective than those which rely on pure strength, such as body-building. The same goes for systems: agile, flexible, and adaptive systems are more likely to be able to return to a sustainable state than monolithic and tough systems. There are many theories that have been developed and new ones in development about Resilience, and whole organizations that focus on it, like the Stockholm Resilience Center and the Resilience Alliance. Please note that the word can be used with different meanings in different professions such as psychology, engineering, and ecology. Thinking in Resilience Resilience is a powerful concept: the degree to which something can withstand unexpected occurrences. Resilience is key for ensuring continuity of a system in the long run. By focusing on resilience, you come to the insight that goals such as growth and profit lead down fragile pathways which, in the end, serve nobody. Replacing these with Resilience as a goal at the topmost level of decision making leads to profound improvements in system performance for all stakeholders, creates clarity where there previously was none, and leads to more effective strategies in the short and long run. Behavior Resilience is a complex system indicator, influenced by a wide variety of network and object parameters, which change depending on the circumstances and context. Resilience tends to be more important for the sustainability of the system as a whole. In general, and in virtually all cases, higher resilience is good. Resilience is highly susceptible to system dynamics, often more so than Autonomy and Equity. It is the most strongly influenced by the network parameters of the three RAH indicators, and less directly by ELSI indicators. Resilience responds to Autonomy and Harmony strongly, but not in a linear fashion. Increased Harmony usually leads to higher Resilience, and a very low Autonomy also hurts Resilience, although a very high autonomy can also cause a low resilience. We’ll look further into the relationships between the network and the system parameters more in the next chapters in the network parameter chapters. Cities have become the central framework to sustain human life. While growth may seem an attractive option for cities, considering the potential economic benefits, we are well aware that growth alone leads to undesirable cities. Applying resilience as a strategy instead of growth is a more effective means to arrive at both economic growth, and an actually flourishing city. A city wanting to improve itself can see that growth isn’t necessarily a good thing. Growth will fluctuate, and end. While a growing city increases resource flows, this can only be temporary, and eventually it will either shrink or run into a resource shortage. This is also because with increased size, resource consumption tends to grow in a non-linear fashion, as well as overhead costs for infrastructure and management. This means that ensuring quality of life, safety, and healthy living environments during growth is no trivial task. What will steer that, what is its target? And when growth ends, the challenge then becomes how to flourish in either condition of growth, while becoming resilient for the long run. If the city focuses on resilience rather than growth, the result is, as a necessary consequence, a more diversely-mixed, flexible, and dynamic urban landscape. It will have less monocultures of office parks and suburbs (which eventually drive traffic congestion out of bounds). It leads to dealing differently with resource management, this costs in investment terms, but pays off in the long run with clean, healthy living conditions, lower utility and maintenance costs. It will deal with the uncertainty of fluctuation resource flows in the future, will restructure itself to allow for changes in demographics and lifestyles, and start to shape itself according to its inherent strengths. When implemented effectively, this leads to better living conditions for all inhabitants, creating a more attractive city, attracting more businesses and investment, and which then causes economic growth. The result is a different trajectory, that leads to a completely different, better performing city, while at the same time leading to better economic conditions. Rather than growth as a driver for economic wellbeing, resilience is used as the driver instead. Economic health is achieved through a different pathway, that simply regards economic growth as a side-effect of being a great, beautiful, healthy, and future-proof place to live and work. Creating such as ‘resilience’ policy will automatically start to influence other areas of systemic concern as well, such as energy, sanitation, poverty alleviation, infrastructure, and cultural programs. For example, it leads to a city in which health is increased by means of biodiversity growth. It means reduced transport loads by smarter reallocation of workforce distribution. These are win-wins that aid to in concert, like instruments in an orchestra, help to achieve benefits that drive the larger goal. This way, a resilience driven strategy maximizes positive benefits on the United Nations SDG program, which helps to acquire investment funds and to achieve positive PR and worldwide recognition. Let’s have a look at what happens when applying resilience as a central strategy for an organization. We can see the same pattern with organizations a we see in cities. For example, if a company, like most traditional companies do, focuses on maximizing profit and growth, it may succeed in doing so for a while. That is, until something unexpected happens which it did not prepare for (not unthinkable in our volatile economy), and it’ll be costly to survive the unexpected blow. The most common method that is used to try to evade this is trend analysis, a form of predicting the future through historic analysis. Based on these trends, the expected outcomes are used as a forecast to strengthen the company where it’s deemed to be weak. But, of course, reality doesn’t necessarily follow the trends, because trends cannot predict the future. Because complex systems can’t be predicted well, and can rapidly change states, this is a poor form of preparing for the future. Many companies can attest to this in the last few decade due to the economic crisis, and many can’t anymore because they’re now gone. If companies focus on resilience as a primary goal instead of growth, an entirely different strategy results. This strategy is based on how to ensure the company can use system dynamics to its advantage given any number of possible situations, by building its resilience to be healthy in the long run. This may include aspects such as having a healthy, diverse, and flexible labor force as opposed to pure efficiency or growth as a focus for HR management. This allows the organization to quickly change its operations and be more resilient for rapid market shifts. Reducing its reliance on limited resources creates economic resilience, which can be achieved by for example switching to bio-based materials instead of fossil resources, taking control of the material cycle, or establishing an industrial symbiosis with supply and delivery partners. This can be done by, for example, focusing on maximizing the company’s value for society and embedding this value, so that the company becomes unmissable for society. For example, it may invest in better living environments, education, and closed loop resource flows rather than entering new markets or on saving labor costs. When a company becomes unmissable for society it has a healthier long term position, and a greater human capital base to carry it forward in rough times. This is something akin to the saying “If you focus on cost, quality goes down, but if you focus on quality, the costs go down.”. example: resilience in cities example: resilience in organizations System Level: Autonomy Autonomy, or self-reliance, is the level of independence of the system from other systems on any level, including material resources, decision making, trade balances, etc. Autonomous systems are less influenced by what happens around them than dependent systems are, freer to follow their own decision trajectory, and can tune their mechanics to suit their own needs more accurately. All systems are dependent on others to some level, which is not necessarily a bad thing, and it’s rarely worthwhile to maximize Autonomy. After all, we’re all living on this planet, and we’re all dependent on the sun and the earth to supply us with the basic platform of life (suggestions for living off-planet notwithstanding). To increase the sustainability of a system it’s usually an effective strategy to increase the autonomy of vital resource flows, network functions and system relations. For this, it then becomes necessary to focus on what these vital resources are, which is a worthwhile exercises in and of itself. Lessons from Autonomy The Autonomy indicator is largely reliant on the physical world around us, affected mostly by the EL part of the ELSI stack. It’s greatly affected by resource availability and the unequal distribution of resources on our planet. By investigating autonomy, we automatically discover local strengths and context-specific solutions to universal demands. This leads us to closed resource loops and cycles, reducing waste and increasing the recognition of value that lies within everything around us. This ties into themes such as the circular economy, blue economy, and bio-based economy. What we learn from Autonomy is the necessity to discern between critical resources and those of luxury. Critical resources of a city, such as food, shelter, clean water, and power, are best to locally provided for, decentralized, adapted to local conditions, and closed loop. Doing so increases resilience and the ability for these needs to be provided in a context sensitive manner, minimizing externalizations such as pollution and injustice. Behavior Autonomy is the system parameter most directly influenced by the lower object parameters of ELSI. Resource cycles heavily affect systems in their ability to be autonomous. In addition, decision making structures are an important factor in autonomy, as well as the balance of interchange between neighboring systems. Per resource, the scale of its optimal autonomy is different. For instance, it’s feasible for small villages to capture and clean their own water, and valuable to do so. However, the same does not apply to building cars. Not every village needs its own factory, even though they may need cars (and then could use a garage). Autonomy needs to be balanced. This can be done by prioritizing essential resources as well as their frequency of use, and their cost of provision. It is similar for policy decisions. Some decision types that are universal and slow to change, are best taken on a centralized level such as the European Union or the United Nations. These are likely to be few, but critical, such as the universal declaration of human rights. Local laws and conditions are usually best to decentralize as much as possible, to increase speed, decrease overhead, and to retain autonomy. System Level: harmony Harmony, or social justice, is all about fairness: to each other, to future generations as well as to all other living things. Harmony is a measure of tension inside of the system, when it is low, there’s a chance for internal collapse through revolution or strife. Harmony touches on the fundamentals of human interaction, and provides the base motivation and conditions for people to want to be part of a system, to make it successful, and thrive in it. In evaluating Harmony, basic human rights come first, which is still a challenge in any global supply chain in the world today. For more complex social and interpersonal evaluations of Harmony, we can use areas of study such as ethics to help, including deontology and consequentialism. Lessons from Harmony History shows us that humanity has the capacity to be unimaginably cruel, both to itself and to other living creatures. We need to protect ourselves from our own dark side. This is what many governmental systems aim to do, to optimize our best behavior, while preventing our worst. Just as with Autonomy, Harmony seeks a state where there’s a healthy playing field to achieve this. We’re far from reaching that state. While we’re protecting ourselves from our worst, we’re also damaging fundamental freedoms that allow the best to surface. We can only go so far in protecting a group at the cost of the individual. At the same time, many structures within our society currently lead to a growing imbalance in power division. Balancing power, and finding equitable mechanisms to steer clear of the atrocities that continue to plague our collective actions is a primary cause for any system hoping to be sustainable. After all, one can have a resilient, autonomous system built on the back of slaves, only serving to propagate the suffering of many, to benefit few. What we see in western society today is a deep layering of hidden social injustice. While not apparent for most consumers, slavery and other gross social injustice is still very much part of this world. It is directly, but invisibly, fed through our consumer supply chains. Many products consumed in the western world are produced elsewhere, and harbor these social wrongdoings. However, they do not even need to injustices from far away. New rings of socially unjust working practices are discovered in the west on a regular basis as well. Efforts such as fair trade improve these, but it is a voluntary practice still in the minority, and for many types of work such a system does not yet exist. Therefore, tracking Harmony through the cause and effect chain is a critical component of working on sustainable systems. Using the SiD framework by creating system maps of these influences can be a great help in tracking down dark spots, to shed light on them. In this way, you can help create increased social justice for whole sectors and product life cycles. Behavior In a world that is increasingly trying to be transparent, we may hope to move towards a world with greater social justice and harmony. Yet, some of this transparency also exposes much wrongdoing not seen before. At the same time, global wealth (and thus power) is becoming more concentrated in ever fewer agents, which obstructs Harmony, and leads to rising tension. These rising tensions are palpable, and result in various systemic effects we can see on a daily basis. These include the Brexit process in the UK, the rise of the extreme right and populism in many western political systems, mass migration and at the same time reduced empathy for immigrants, and terrorism. Harmony is primarily fed by the top layers of ELSI, such as cultural rules, laws, economic balances, and the health and happiness of those that are inhabiting the system. These object level aspects connect to Harmony network parameters, such as Balance of Power, and Equity. Besides Harmony’s own network parameters, Harmony can also be affected by the resilience network parameters, such as awareness, transparency, and validity. Increasing these network parameters through law or policy will lead to increased Harmony in the long run. Harmony is typically tracked in space and context for social justice issues of today. Mapping Harmony in time leads to questions concerning future generations, as well as learning from past mistakes. From the dark to the light Harmony is an immensely powerful aspect when used in the right way. If you internalize that Harmony is not only about preventing harm and to stop injustice, but about boosting positive developments as well, new avenues open up. We can achieve great systemic improvements when systems are tuned towards aspects such as happiness, balances between freedom and responsibility, and the support structures to guide these in a healthy way. These Harmony dynamics can make systems move by harnessing the internal tensions for good, by engaging internal willpower, passion, and fascination. In many cases, when smartly implemented, positive impacts on Harmony can be achieved in most projects with little effort, and a great return on investment. Harmony can also be seen as a measure of how much each individual can strive for personal flourishing in a system. For example, if a company is able to channel the passion and dedication of its employees to create value, it will be a vastly more effective organization. Harmony’s dynamics live deeply rooted in our society. It’s fast-moving, and can shift focus rapidly. In the last decades we’ve seen issues such as poverty, gender equality, LGBTI rights, labor practices, and many more receive significant global attention, much to the benefit of specific communities and human rights in general. In this light, Harmony and Resilience go hand in hand, sharing the needs for transparency and awareness first and foremost in the path towards sustainable change. Time Daylight Traveler Food Ecosystem Advertisement put in picture of network evaluation sheet bottom-up network exercise Imagine a social network. Let’s call it Numbernet. Numbernet can be used to connect to friends online. Now, do the following three things: Only considering the social network aspect (not the technical aspects): go down the list of 9 resilience network parameters one by one, and determine what simple formula would be useful to inform each network parameter. Do the same, but now only considering the technical aspects (devices, servers, etc). Point to the network parameters that are likely to influence the success of the network most. Examples Connectivity The total amount of users multiplied by the total amount of connections between all users. Awareness The number of users that will see a message of importance that is posted by a single user in a 24 hour period.

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The time it takes for an important message to reach 90%+ of the network’s users. Redundancy The number of devices a single user can access the network on.

• or - The number of backup servers for each critical node in the technical system

Flexibility The time it takes to make or break connections between users. Transparency The time it takes for a message to travel from one user to another.

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A calculation of all messages multiplied by the amount of people allowed to see the messages, divided by the total amount of messages. Diversity The total amount of users divided by the different types of users in a certain chosen category (age group, nationality, etc). Centrality The median number of connections per person divided by the average number of connections per person. Complexity The total amount of users multiplied by the total amount of connections. Validity The total messages transmitted to users divided by the network’s consensus (or scientific consensus) of the validity of the messages. Top-down network exercise Take an imaginary country, which we will call Billistan. Conjure up an image of a small country with a few dozen towns about a few hundred years ago and see which resilience network parameters you can influence by adding a modern technology, or policy measure. Do this for all the network parameters. news: connectivity and awareness Imagine that there is no form of news propagation in Billistan: no radio, TV, newspaper, or otherwise. This means the ‘connectivity’ network parameter is low, and subsequently also the ‘awareness’ indicator of Billistan’s network is low. Its effect could be, for instance, that one town within Billistan will only hear of another town’s failed harvest if someone from one village happens to pass through and deliver the news to the other village. This may result in famined villages collapsing, while they could have been helped by other towns (and in return help with something else the next year). So, we can conclude that this reduced connectivity and awareness reduces the resiliency of the country as a whole. Adding a news network to Billistan increases its connectivity and awareness, and if the news stays true (validity), it will withstand something like famine better, and thus increase its resilience. This comes at a cost of efficiency, because some people need to dedicate themselves to making news, and can no longer produce resources. Effects of censorship In another example, consider an oppressive political system which censors bad news. This lack of transparency produces a great disparity between perceived awareness and reality. Consequently, this reduced awareness harms resilience. Censorship reduces the transparency of the news to such a degree that bad news is not allowed to be reported, and the actual awareness value of the news is reduced. In this case, it may be even though of as worse, because perceived awareness and actual awareness may diverge, possibly reducing the effort to increase awareness. Diversity of education Consider a different resilience network parameter - diversity. If Billistan has a low diversity of education, social class, and background, it’s conceivable that there is great consensus in opinion and perspective. This in turn likely generates a single echo chamber which jeopardizes resilience. Diversity often positively contributes to resilience. For example, a diverse population provides varied know-hows that could make or break its emergence through a crisis. Diversity in life-styles and population demographics increases resilience through disease-resistance, increased creative power, and flexibility. the resilience network parameters CRAFTDCCV resilience network, CRAFTDCCV in Detail The resilience network parameters concern the whole of the collection of objects and their inter-object relationships, in time and space. These nine holistic network parameters look at the composition of the network, and together they help to determine a system’s resilience. In this next section we explain the meaning of these parameters and some of our experience with what they mean for creating a resilient state of a complex dynamic system. The 9 resilience network parameters are: Connectivity Redundancy Awareness Flexibility Transparency Diversity Centrality Complexity Validity In some cases, it helps to subdivide these 9 parameters according to types, as follows: Structure These three parameters mostly deal with how the network is structured: the amount of connections, and the network’s (physical) structure. Connectivity Redundancy Centrality Character These three parameters most often deal with the character of the system’s network; how fast it can react, and the diversity of its composition. Flexibility Diversity Complexity Content These three parameters reflect on the content of information traveling around the network. To what degree nodes are aware of this information, the speed of transfer, and truthfulness of information. Awareness Transparency Validity “For every complex problem there is an answer that is clear, simple, and wrong.”

• H. L, Mencken