RESOURCES // ABSTRACT

Our world is one of limited resources. This is more true today than ever. Owing to over population and unchecked consumption, we are fast depleting our most essential resource - water. The U.N. estimates that 1.2 billion people live in areas of water scarcity and another 1.6 billion people face economic water shortage. (United Nations Environment Programme, n.d.).

Architecture is not divorced from these pressing concerns. In fact, the environmental impact of buildings is well documented. “Green” strategies are certainly beneficial in the construction of a more sustainable built environment. But what about design itself? How do we as architects conceptualize a resource-centric design? We can add solar panels to any roof but that is only a topical treatment for a serious wound.

This thesis proposes two theories on an architecture of restraint. The first is based on an understanding of ornament from a biological perspective. The second borrows other forms of natural intelligence that have a propensity for stability. The hope is that these new ways of thinking about design will help push us towards a balanced architecture.

ENVIRONMENTAL IMPACT OF BUILDINGS // 65% of total U.S. electricity consumption // 40% of total U.S. primary energy use // 30% of total U.S. greenhouse gas (GHG) emissions // 40% of raw materials used globally (3 billion tons annually) // 40% of landfill material in the U.S. (136 million tons annually) // 12% of potable water in the U.S.

Fig. 1 Satellite image of earth settlementsNote the urbanization near water sources, including the Nile River and Nile Delta in Egypt. In this dry part of the world, surface-water supplies are essential for human communities. (See http://ga.water.usgs.gov/edu/watercyclefreshstorage.html).

STRUGGLE FOR EXISTENCE // CONTEXT

How is balance achieved in other contexts? We may look to the natural world for one perspective. In nature, the conflict between a state of limited resources with the demands of population growth produces what Charles Darwin termed “the struggle for existence.” (Darwin, 1859).

Darwin observed that animal and plant populations have an immense potential for growth. For example, even the elephant, one of the slowest mammalian breeders, could overrun the planet if it were allowed to reproduce unhindered. After only 500 years, one pair would leave 15 million descendants.

Elephants do not run rampant, Darwin tells us, because the Doctrine of Malthus applies in manifold force. T. R. Malthus, in An Essay on the Principle of Population, states that “the superior power of population is repressed, and the actual population kept equal to the means of subsistence, by misery and vice.” (Malthus, 1798). Unlike humans, animals and plants cannot artificially boost their food supply, nor can they practice restraint in breeding. Therefore, “although some species may be now increasing, more or less rapidly, in numbers, all cannot do so, for the world would not hold them.” (Darwin, 1859).

This lack of resources produces an intense competition for survival, which works to keep populations and subsequent resource demands in check. This struggle for existence may be a conflict for food or a battle against the elements. No matter the form, it is ever present. “All that we can do is to keep steadily in mind that each organic being is striving to increase at a geometrical ratio; that each at some period of its life, during some season of the year, during each generation or at intervals, has to struggle for life, and to suffer great destruction.” (Darwin, 1859).

Fig. 2 Fish from the voyage of the H.M.S. BeagleExquisite variation in nature only becomes full species through “the struggle for existence.” (Darwin, 1842).

Fig. 3 Tree of LifeDarwin indicates the “divergence of character” from original species into new species via a tree diagram and calculations, the only illustration in the Origin of Species.

TRAGEDY OF THE COMMONS // CONTEXT

Technology dampens the effect of Darwin’s “struggle for existence” in the human context. In other words, the use of technology compensates for this state of limited resources. Yet, like an over-population of elephants, humans are prone to destroying our environment and resources through an unbalanced use of this technological power. Garrett Hardin describes this phenomenon as “The Tragedy of the Commons.”

According to Hardin, this dilemma arises from the situation in which multiple individuals, acting independently and rationally consulting their own self-interest, will ultimately deplete a shared limited resource even when it is clear that it is not in anyone’s long-term interest for this to happen. (Hardin, 1968).

This Tragedy of the Commons has been observed in many instances where water resources are devastated by human intrusion, including the diversion of water from Mono Lake and Owens Lake to feed the Los Angeles aqueducts, and the draining of the Aral Sea. In these examples, there is depletion of fresh water, over fishing, contamination and destruction of habitat.

Fig. 5 Aerial Image of Mono LakeThe shrinking water level also caused Negit Island to became landbridged, exposing the nests of gulls to predators (chiefly coyotes) and forcing the breeding colony to abandon the site.

In 1974, Stanford University graduate student David Gaines studied the Mono Lake ecosystem and was instrumental in alerting the public of the effects of the lower water level. This effort helped spur the California State Water Resources Control Board into action; in 1994, it issued an order to protect the lake and its tributary streams. Since that time, the lake level has steadily risen.

Fig. 4 The Tufa Towers of Mono Lake (opposite)Mono Lake is a freshwater lake approximately 300 miles from Los Angeles. In 1941 the Los Angeles Aqueduct system was extended farther upriver into the Mono Basin. So much water was diverted that evaporation soon exceeded inflow and the surface level of the lake fell rapidly. By 1982, the lake had lost 31% of its surface area. As a result, alkaline sands and once-submerged tufa towers became exposed.

TRAGEDY OF THE COMMONS // CONTEXT

There have been many proposed solutions to the Tragedy of the Commons. One such proposal relies on private property rights: convert common goods into private property, giving the new owner an incentive to enforce its sustainability. This is an argument often made in water rights, but many times private ownership results in worse outcomes compared to common management. This is partly because water is difficult to privatize due to its flowing nature. It is also difficult to privatize because other property components, such as fish populations, are wound up with water as a resource.

This difficulty with how to privatize a common good was somewhat resolved when Ronald Coase put forward the Coase Theorem (which was awarded the Nobel Prize in Economics in 1991). According to this theorem, the initial imposition of legal entitlement is irrelevant because the parties will eventually reach the same result. This is because the party able to reap the higher economic gain from a resource would have an incentive to pay the other parties not to interfere. In the absence of transaction costs, the parties would strike a mutually advantageous deal in how to use a resource. Coase, of course, pointed out that in most situatios transaction costs could not be neglected, and therefore, the initial allocation of property rights does indeed matter.

If we cannot learn to share a resource, and we cannot easily allocate ownership of a resource, then we are forced to rely on renewable and sustainable technologies to help make more of a resource. Such technologies help turn a zero-sum scarce resource into more of a renewable resource, thus alleviating the individual demands. However, a renewable resource can still become de-valued because of over-exploitation.

Hardin focuses on problems that he feels cannot be solved by technical means, i.e., as distinct from those with solutions that require “a change only in the techniques of the natural sciences, demanding little or nothing in the way of change in human values or ideas of morality.” Hardin contends that this class of problems includes many of those raised by human population growth and the use of the Earth’s natural resources. “The population problem has no technical solution; it requires a fundamental extension in morality.”

Fig. 6 Abandoned Boat in the Aral SeaSimilar to Mono Lake, the Aral Sea has also seen its water levels drop dramatically. Situated between Kazakhstan and Uzbekistan, the water from this lake has been steadily shrinking since the 1960s after the rivers that fed it were diverted by Soviet irrigation projects. By 2009, the southeastern lake had disappeared and the southwestern lake retreated to a thin strip at the extreme west of the former southern sea.

The shrinking of the Aral Sea has been called “one of the planet’s worst environmental disasters.” The fishing industry has been essentially destroyed, bringing unemployment and economic hardship. The region is also heavily polluted, causing serious public health problems. The retreat of the sea has reportedly also caused local climate change, with hotter, drier summers and colder, longer winters.

Fig. 7 Satellite Image of Owens LakeOwens Lake did not fare as well as Mono Lake. After almost a century of being diverted into the L.A. aqueduct system, the lake is now desiccated. This image shows the mostly dry bed of the remaining lake. Periodic winds stir up noxious alkali dust storms that carry away as much as 4 million tons of dust from the lake bed each year, causing respiratory problems in nearby residents.

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ORNAMENT AS HONEST SIGNALS PEACOCK SOUTH ASIA

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ORNAMENT AND HONEST SIGNALING // THEORY

As discussed above, the natural balance provided by the “struggle for existence” is weakened in the human context due to mankind’s technology and cleverness. So we habitually over consume and destroy our natural resources. This tragedy is difficult to avoid, even with legal, economic and social constructs. As Hardin suggests, we must fundamentally recognize this dilemma and actively struggle to avoid this outcome. Understanding the role of ornament in architecture can help achieve this goal.

This thesis hypothesizes that ornament functions as an honest signal in the context of architecture. Wherever there are organisms, there are signals. In nature, animal calls, patterns, colors, fragrances, are all modalities by which signals are sent and received. These signals function to indicate some property of the organism, such as its overall condition or its genetic quality. If we add a cost to the production of these signals, whether it is measured in energy, time or some other externality (such as increased danger from producing the signal), then the signals will inherently tend to be honest.

In the early 1970’s, biologist and natural historian Amotz Zahavi termed this the “handicap principle.” (Zahavi, 1997). He suggested that there is something about costly behaviors or physical features (or “handicaps”) that make for inherently reliable signals. For example, a peacock’s tail may be a signal used by prospective mates in order to estimate the individual’s overall condition and/or genetic quality.

Fig. 8 Peacock PlumageWhen choosing a suitor, it is difficult for peahens to judge a male’s genetic quality directly. Instead, peahen attend to signals that the males provide; namely, its bright plumage and long flamboyant tails. This ornament is a handicap because it is energetically costly to produce, and it is dangerously conspicuous as well.

This cost ensures that the signal is honest. A weak and sickly male cannot afford to divert energy from basic upkeep to the production of ornament; moreover, a long tail would make him more susceptible to predators. A strong and healthy male, by contrast, can readily afford the additional costs of producing bright colors and a long tail, and can more easily escape a predator even when burdened by the length of his tail.

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ORNAMENT ASHONEST SIGNAL

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Fig. 10 Phases of OrnamentationDuring the tail phase, ornamentation may be thought of as “crime” in the Loosian sense. In Ornament and Crime, it struck Loos that it was a crime to waste the effort to add ornamentation, when the ornamentation would cause the object to soon go out of style. Ornament viewed from the perspective of honest signals clarifies this position: it is not so much that ornament inherently causes an object to become unfashionable but rather that it no longer functions properly to indicate the value of the object.

PHASES OF ORNAMENT // THEORY

Like the feathers of a peacock, ornament may function as an honest signal in architecture. Ornamentation is certainly costly to produce, and the ability to do so may initially indicate some sort of innovation or efficiency. Honest ornamentation may therefore induce others to select this particular form of building, leading to the further development and articulation of a type of architecture. Over time, this ornamentation may become codified. Like wealth, it may become a virtue in its own right, and no longer an accurate signal of the original benefits.

This cycle of ornamental function has consequences on resource expenditures. Initial expenditures during the period when ornamentation still functions honestly is arguably a positive investment of resources. The subsequent phase of codification could be viewed as a neutral period, neither beneficial nor detrimental. However, when ornament is no longer an accurate indicator of quality, efficiency or innovation, then the further allocation of resources to it becomes an unworthy investment.

The negative impact on resources during this tail phase is enhanced by the geometric nature of population growth. Population exceeding the carrying capacity of an area or environment is called overpopulation. Overpopulation exacerbates problems such as pollution and resource management. Our choices on what to build, and how to build, are all the more important given this state of overpopulation. To achieve a more balanced architecture, we must be cognizant of the role and effect of ornament and respond to situations accordingly.

Ornament may initially serve a secondary function as an honest signal. Over time, it may be strengthened for its stigmergic aspect, evetually becoming codified as a decision making shortcut.

SPANDREL AREA

STRUCTURAL STRESS

STRESS-TO-SPANDREL OPTIMALITY POINT

SPANDREL ORNAMENTATION STIGMERGIC ITERATION STUDIES

Ornament may initially serve a secondary function as an honest signal. Over time, it may be strengthened for its stigmergic aspect, evetually becoming codified as a decision making shortcut.

SPANDREL AREA

STRUCTURAL STRESS

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SPANDREL ORNAMENTATION STIGMERGIC ITERATION STUDIES

Fig. 11 Spandrel OrnamentationOrnament may initially serve a secondary function as an honest signal. Over time, it may be strengthened for its stigmergic aspect, eventually becoming codified as a decision making shortcut.

PARALLEL SYSTEMS // THEORY

As discussed above, the amplifying quality of nature has the potential to produce destructive explosions. This positive feedback is kept in check by a state limited resources. There are, however, other natural systems that have built-in mechanisms that keep positive feedback under control. Many of these are parallel systems that exhibit self-organization. A more balanced architecture can be achieved if we learn to incorporate self-organizing and parallel design concepts.

A parallel system is one that is composed of many individual units that operate in parallel, using only localized information. The units can be of different complexity but they are often entities with limited perceptions of its environment “that can process information to calculate an action so as to be goal-seeking on a local scale.” (Flake, 2000) The main quality is that entities operate in a parallel, non-sequential manner.

In many parallel systems, negative feedback plays a critical role in providing a brake on over amplification. Consider bluegill fish where males tend to nest near one another. Why are bluegill colonies not regularly overcrowded? Research suggests that negative feedback limits their behavior tendency to nest closely together. In other words, bluegill males follow a rule such as: “I nest where others nest, unless the area is overcrowded.” In this case, both positive and negative feedback are coded in how bluegill fish units operate. (Camazine, 2003).

In fact, it is the interaction between positive and negative feedback that produce many of the striking patterns in nature. As a result of the bluegills opposing tendencies to gather together yet maintain personal spacing, the breeding ground becomes a beautiful closely packed polygonal array of nests.

Camazine explains that this pattern “itself serves no function and has no adaptive significance. Instead, the regular geometric spacing of nests probably is an epiphenomenon, an incidental consequence of each individual striving to be close, but not too close, to a neighbor. Mechanistically, it arises automatically through a self-organizing process similar to the hexagonal close-packing of round marbles placed in a dish.”

Fig. 19 Tilapia Fish NestsTop view of the polygonal pattern of male Tilapia nest territories, which are similar to bluegill colonies.

Fig. 20 Tilapia Fish NestsImage of Tilapia nests from an aquaculture hatchery.

PHYSICAL EXPERIMENTATION // PRACTICE

The process of physical experimentation is one way to work with parallel systems in nature such as slime mold. This process of physical experimentation is key in developing human knowledge and illustrates how science interacts with the universe. In this process, physical experimentation is used to engage with the natural phenomenon. Observations of experimental results lead to human understanding that models the natural world. These models can then be manipulated “to see if they make accurate predictions of future observations.” (Flake, 2000).

Here, slime mold experiments were conducted to explore their networking abilities. These experiments tested a variety of slime mold preferences, including: (1) distance to food; (2) amount of food; and (3) distance to food versus quantity of food. In the first experiment, slime mold was placed in the center of a petri dish. Food was then placed at nine other random points around the slime mold. Observations were then made to see what type of network the slime mold would make given these points of food. Initial results show that the slime mold connected the nine points in order of distance. First, a branch was extended to the closest food location, point A; the mold then extended a second branch to point B, the point closest to A. This process continued until all of the points were connected.

The second experiment tested how the amount of food affects slime mold behavior. Again, slime mold was placed in the center. Two points of food were then placed equidistant to the center on the edge of the petri dish. One point had twice as much food as the other. In this context, the slime mold first branched out uniformly in all directions, searching for food. After it discovered both food sources, however, the slime mold started to adapt and tune its branches. Eventually, the mold abandoned the network to the smaller amount of food, and instead created one thick vein to the large food source.

In the third experiment, the mold was placed at the edge of the petri dish. One food source was then placed at the center, and a second larger food source was placed at the edge, opposite the mold. Thus, the mold was presented with two options: (1) a smaller amount of food that was closer in distance, versus (2) a larger amount of food that was farther away. The slime mold, again, initially starts off searching in all directions. Once the smaller food source was found, the network was consolidated into one

thick branch to this food source. From here, the mold again spread in search of more food, where it eventually discovered the larger food source.These experiments suggest that the slime mold searches in two distinctive modes. Initially, the slime mold branches in a uniform manner, searching in all directions for food. When food sources are found, the slime mold begins to make interesting decisions. In the case of a single source, the mold will consider the amount of food in determining how much to continue searching. Where the food source is large, the slime mold will slow or stop moving; where the food source is small, the slime mold will continue to branch and spread, looking for more food.

When multiple food sources are found, the mold appears to tune its network according to the amount of food at each source. For large sources, the mold’s connecting network consolidates: branches are pruned and disappear, leaving a thicker tube with few offshoots. For smaller sources, sometimes a thin branch is maintained, while other times the entire branch is retracted and eventually eliminated.

Fig. 25 Networking Experiments (opposite)This set of 4 experiments tested the mold’s general abilities in growth and networking.

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DIGITAL EXPERIMENTATION // PRACTICE

With the advent of digital computation, designers now have digital simulation as an additional design tool. To simulate is to simultaneously engage in theory and experimentation. Through simulation, we can put our theories to immediate test, leading to more observations and understanding. Simulations also allow our models to interact with other digital processes; this effectuates an interdisciplinary environment that enables discoveries that are relevant to multiple fields.

With slime mold, the observations gained through physical experimentation can be used to simulate its behavior. This digital intelligence can then be injected into other digital processes to see how slime mold behavior can interact with and affect other system components. For example, digital slime mold can be integrated with structural, thermal, sunlight and other analyses.

The screen shot images on the right are the initial attempts at simulating a slime mold that branches according to topographic variation as input.

INITIAL TOPOGRAPHIC CONDITION AS INPUT

BRANCHING SNAPSHOTS

Fig. 28 Slime Mold Digital Simulation (opposite)A digital simulation of slime mold branching was designed based on the observations from the physical experiments. In this simulation, the branches are composed of individual “units” that act according to their internal state, the state of a limited set of neighbors, and input conditions.

Here, the input parameter to the units is the slope of a site and branching rates andz direction are partially determined based on this information. The simulation is implemented in Javascript using the new WEBGL library available for web browsers.

ANALOG COMPUTATION // PRACTICE

To use this slime mold computer, it first had to be calibrated in terms of determining how much food is exactly required to get a specific type of growth output. This was done via a third set of slime mold experiments where each discrete food concentration level was cast in agar and mold grown for observation. In total, 30 different combinations of food were used to calibrate the slime mold computer, ranging from 0 to 3% nutrient agar.

Each of these percentages were cast in an individual petri dish for experimentation, and then a single casting incorporating all food combinations was cast to see how the mold would grow across these food gradients.

0 to 3g of Agar for 100ml solution:===============-13: .11g of nutrient agar (“NA), 1.89g non-nutrient agar (“NNA”)-12: .22g NA/1.78NNA-11: 0.33g / 1.67-10: 0.44g / 1.56 -09: 0.55g / 1.45 +-08: 0.66g / 1.34 +-07: 0.77g / 1.23 +-06: 0.88g / 1.12 +-05: 0.99g / 1.01 +-04: 1.10g / 0.90 +-03: 1.21g / 0.79 +-02: 1.32g / 0.68 +-01: 1.43g / 0.57 + 00: 1.54g / 0.46 ++01: 1.65g / 0.35 ++02: 1.76g / 0.24 ++03: 1.87g / 0.13 ++04: 1.98g / 0.02 ++05: 2.09g ++06: 2.20g ++07: 2.31g ++08: 2.42g ++09: 2.53g ++10: 2.64g ++11: 2.73g ++12: 2.84g +

Fig. 30 Calibration Experiments (above)These show 4 calibrations for 0.66NA, 1.32NA, 1.98NA and 2.42NA. Different types of growth is observed depending on the amount of nutrient used in the specific experiment.

Fig. 31 Resource Gradients (opposite)Inspired by John Hejduk’s 9 squares problem, the different percentages of nutrient were cast into nine circles, following a topographic pattern. This allowed the mold to grow and differentiate itself according to the varying amounts of food.