Does “the best time to plant a tree was twenty years ago and the second best time is now” date back further than 1988?
It is time to look long and hard at the value of the urban forest and create the broad-based efforts — in research, funding and citizen participation — needed to improve it. The lesson is, the best time to plant a tree was twenty years ago and the second best time is now.
The rising prices of Christmas trees in 2019 surfaced this question.
Consumers on the hunt for a Christmas tree have little to cheer about this year, as prices are through the roof due to a shortage of trees that can be traced back to the 2008 financial crisis.
The Great Recession put thousands of American Christmas tree farmers out of business, resulting in far fewer seedlings being planted. As trees have a maturity cycle of 10 years, the lack of supply is just now beginning to bite, pushing up U.S. demand for Canadian Christmas trees and causing higher prices for consumers across the continent. [….]
Paul Quinn suspects the supply shortage will remain for at least a couple years.
“As the economics get better for tree growers you’ll see them planting more trees. Unfortunately, you had to have that foresight 10 years ago,” he said.
In the pursuit of etymology and the better quote citation, I would welcome seeing earlier uses of the phrase!
Broadly, data science is an interdisciplinary scientific approach that provides methods to understand and solve problems in an evidence-based manner, using data and experience. Despite the clear benefits from adoption, many firms face challenges, be that legal, organisational, or business practices, when seeking to implement and embed data science within an existing framework.
In this workshop, panel and audience members draw on their experiences to elaborate on the challenges encountered when attempting to deploying data science within existing frameworks. Panel and audience members are drawn from business, academia, and think-tanks. For discussion purposes the challenges are grouped within three themes: regulatory; investment; and workforce.
The regulatory framework governing data science is outdated and fragmented, and for many new developments, regulations are in a state of flux, or non-existent. This creates an uncertain environment for investment in data science and can create barriers to the widespread adoption of state-of-the-art data science. For instance, the governance of data use and data sharing are unclear, and this may compromise trust in data. Additionally, privacy laws, currently under scrutiny in many countries, may limit how firms can use data in the near future affecting innovation, and planned investments (e.g., Google Sidewalk). As data science technologies and applications change rapidly, the regulatory framework must continually evolve or risk becoming outdated and a hindrance to developments in the field.
Investment risk exists for any project, however data science projects are especially risky for various reasons, including the fundamental role that datasets play. Creating, cleaning, updating, and securing a dataset is a difficult process that requires a substantial investment of resources. And while these are essential processes in order to extract value from data science, they rarely provide value themselves which can be a challenge when making a business case and investment decision and adds risk to the decision to adopt data science practices especially for small- and medium-sized businesses.
The workforce challenges of data science are extensive. It is difficult to recruit qualified candidates due to the specific skill sets needed, and, with more firms seeking to implement the new innovations, this problem is expected to become worse. Additionally, many fear the lack of diversity in the current pool of workers may hinder progress in cases where the data science applications are context specific and would benefit from subject-matter expertise and a diversity of experience.
Outcomes of the workshop are expected to include a report that lists a set of existing practises and high-level barriers to deployment.
Intro from Rohan Alexander (UToronto iSchool), co-organized with, Kelly Lyons (UToronto iSchool), Michelle Alexopoulos (UToronto Economics), Lisa Austin (UToronto Law)
Data science adoption doesn’t seem to have changed, over the past 5 to 10 years
Legal frameworks, consent issues, interacting with other jurisdictions
Organization challenges: Difficult to add to old organization, lack of qualified candidates, lack of diversity, pipeline issue of graduates going to other countries
Risks: Have to get clean datasets, so rational at 5% makes sense, or allocation of resources?
Submit questions to Slido.com, #L763
This digest was created in real-time during the meeting,based on the speaker’s presentation(s) and comments from the audience. The content should not be viewed as an official transcript of the meeting, but only as an interpretation by a single individual. Lapses, grammatical errors, and typing mistakes may not have been corrected. Questions about content should be directed to the originator. The digest has been made available for purposes of scholarship, posted by David Ing.
Launched by Deloitte 5 years ago
Ran survey, four themes
Found 16% adoption of AI in industry
1. Lack of understanding: Only 5% of Canadians think that they will be impacted by AI over the next 5 years, despite having smartphone.
2. Lack of trust: Data breaches, misuse of data. Killer robots, not what machine learning is about. Boston Dynamics video creates misconceptions. Also chatbots used in customer care, fancy versions of press 1 for this, press 2 for that, yet people use terms like “computers are seeing”. Computer systems as ominipresent, and don’t trust decision-makers.
3. Lack of awareness: In Toronto, ecosystem of startups, but difficult from them to link to enterprise companies. Not getting in front of decision-makers. Enterprises feel risk of dealing with startups that may not be around for few years. Hard to advertise, misuse of language.
4. Inability to scale: Companies don’t know how to adopt. May hire data scientists, but into corner, and think they’ll do cool stuff and make money. Have to think of ROI from beginning. May not have incentives to put into production, after the work is done. Prove to me it works, versus assume that it’s going to work.
Ajiolomohi Egwaikhide, Senior Data Scientist, IBM Systems
What can go wrong? Bad algorithm, or bad data
Customers want to take data, and too cool stuff, but don’t have enough data or right data to solve business problem. Then end up with backlash.
a. Insufficient quantity
b. Non-representative training data, or data isn’t telling them what they’re thinking.
c. Quality of data, has a lot of outliers, noise, missing data. Don’t know what they should be collecting.
d. Irrelevant features: Lots of columns of database, but no business capabilities around them
a. Using fancy algorithms instead of simple models, e.g. survivor algorithm versus simpler logistic regression. Not selling the right thing.
John McCarthy said if it works, don’t call it artificial intelligence.
There’s a lot of adoption, e.g. a smartphone has 100 instances of what we might call AI.
Legal aspects: e.g. supervised deep learning algorithms, in medical imaging, but then issues with privacy and disagreements from experts on labels, should otherwise be solvable.
Risks: Idea of killer robots. Self-driving paradox, if get 10% improvement, would have 1.2M die instead of 1.3M, isn’t a personalized argument.
Technical: From software engineering, coding algorithm, get a precision or metric of interest, you could have messed up, you wouldn’t know, because it’s not testable in the same way as regular software. If can tune parameters, if you don’t have a deep understanding or mathematical intuition, will get people throwing data at it. Irresponsible use.
Algorithms (e.g. Tensorflow) are still experimental, missing debugging, control flow.
Policy: Technology ahead of law. Ethical considerations, e.g. people messing up traffic signs. Will continue working on robustness, but people should go to jail for tearing down a traffic sign.
Productionization: Have data scientists, prototype quickly in a sandbox environment, load, train metrics, and they say it will work. But then to put into a production system, it’s streaming and works in real time. It doesn’t care about models, it cares about output and costs. e.g. build a detector 5% better, but then the car doesn’t work as well. Not good correlation between model-level metric and system-level metric.
Legal perspective (Aaron?)
Barrier to adoption:
(i) Regulatory: Laws are antiquated. Cambridge Analytica, etc., is based on the consent-based model. People don’t read the terms they click on. Transparency. Dealing with disclosure. We don’t know what we’re agreeing to.
(ii) Investment and risk: Big undertakings, expensive. Senior management vs. data scientists. Companies treating data science as just another project. Data quality. Considered in a silo. No architecture for data.
(iii) Workforce, trading and labour market: Requires a lot of expertise, there’s a shortage of people. Difficult to recruit people with skills. Expect to become worse. Lack of qualified labour.
Are there ways that universities could be more involved? Can we build universities, or training protocols, within companies?
Data science will become more important, not less. How to handle?
Panel discussion together.
Clients coming for consultants, because they want to push something forward, and say we can do it ourselves. Data scientists put into a position to just solve this.
Bad reputation on trust and ethics.
Data science equated to steam engine and electricity.
Clients aware that they should be doing something.
What, but then trust and ethics.
In banking, lots of accountability associated with models in production.
Line between statistics and machine learning is blurry.
Struggling with black box approaches
Lack of trust slows us down
How can I do it?
Question: Reputation of data science, and how it impacts work?
When are we going to get the cars off the road?
Companies are heavily invested, but it’s an open-ended scientific problem.
We don’t know how to do it, but then companies say we’re going to have it by end of the year.
Already have assistive technologies that work well if someone is behind the wheel
Robustness at 99.9% in a lot of technologies today
But have a lot of variability today.
If have someone behind wheel that could save from serious errors, or on given route, or under weather conditions, different.
Question: Unsolvable problems. Researchers show examples of recognizing cat from dog, but then expect we can do cars. Problem of lack of understanding, but it’s not zero-one. They’re feeling overconfident. A general vision of what AI could do, but we’re not there today.
Interdisciplinarity is complicated. Executives not making bad decisions, requisite understanding. It’s economic, technical, trade, privacy, transnational. Evolutionary, not binary on-off. Will get to better decision-making frameworks, but will take time.
Question: Reputation. AI is doing their job, not true, could augment. How to correct messaging?
In high school, start of robotics, were promised 4-day work weeks. Predict that there will no such thing as replacement.
People’s jobs change over time. Agriculture. Call centres are replaced by chatbots. If remove 80% of mundane work, but unemployment work isn’t 50% today from agricultural. Problem is over what time frame, 5 years or 15 years.
Question: Automation versus AI?
In school, used term machine learning instead of AI. Now AI is everything. Lots of natural language processing. Technologies are getting more intertwined.
People myopic about technology. Many jobs get created in unpredicted ways, for non-technical people. Gig economy. How many people get married through online dating apps? Using AirBnB, Uber, are rapid changes in life.
People thinking about how should retrain. Retraining programs by Bank of Canada aren’t being used.
Questions: From Twitter — It’s AI when you’re funding. It’s machine learning when you’re building. It’s logistic regression when it’s implemented.
First course in machine learning including logistic regression and linear regression. If you want to call it AI, call it AI, it doesn’t matter any more.
Question: Mining sector, predictive maintenance. Anti-fraud, in banking, 80% of workload is logistic regression.
If you can structure data in a table, use logistic regression. Get stability, robustness, conversion.
Educate customers towards getting real value.
History, neural networks became popular, due to availability of data. Successful on a very small set of applications. Anything that a human being can do quickly, video-audio-words. But have age or income, probably won’t get a neural network that works better than logistic regression. But neural networks would solve problems that weren’t solvable on logistic regression. Reputation, but then people trying to use neural networks where they don’t apply, in video or audio types.
Question: Domain-specific knowledge?
Clients bring a lot of domain-specific knowledge. Haven’t been in a situation where they’ve asked for a specific algorithm. It has to make business sense for them.
Working with data, have to understand that data.
Question: 16% adoption rate?
16% adoption rate in Canada, in small and large businesses.
Question: Trust. Worry about lack of regulations? Moving target? Locking things down?
Smaller companies don’t think about regulatory aspects. Larger companies are working with regulators. Trust issue.
Decisions that are made will become more important. e.g. judge will make judgement, then can verify, and subject to review. A construct to review black box decisions? Proprietary? Can’t review? Mortgage applications, university applications, bail applications.
In Canada, sometimes not taking care of own, e.g. GDPR is in Europe.
Question: Not releasing datasets. Understanding why AI is making decisions.
A lot of companies think their data is their competitive advantage. But at the same time, want to get access to others’ data, so have to share. Startups in Toronto work on how might share insights without sharing data.
For self-driving, Waymo isn’t shared. Trying to figure the best way to go at it. Tough. Sensitivity to cyber attacks.
Emergence of three data blocks of governance.
Geo-political development. China, U.S. and EU moving different ways, other countries haven’t moved. Would like to see Canada take leadership position.
Economic: 85% of top company value is intellectual capital and brand value.
Regulations under constant review?
Lawyers take a principled approach. But things are moving quicker. e.g. data portability, is it mine or company? IoT and sensor data is at the bleeding edge. CCTV cameras that got hacked. Measure twice and cut once.
AI is a marketing word, used badly. 1960s-1970s interpretation of human consciousness. Younger interpretation of things that do things for you, not conscious. Executives want adoption, but not definitions. Black box, magic. When asked about adopting AI, are they adopting heuristic algorithms that marketers would call it.
What problem are we trying to solve? Rate of adaptation, faster or better? Pace of activity is right? Whose problem are we trying to solve? In Canada, have banks, medium-size manufacturing, and lots of small. Need to find a way to have conversations with IT organization, as only going to give budget. Technology has enabled a small number of companies to decimate government, control the way we’re living. Have to look at open source, and then governments take control?
Is there something we can do, if there’s a barrier.
Data science as science.
Research money earmarked as part of IT budget.
Question: Policy in other jurisdictions?
Transnational, also in NAFTA 2.0. Constraints by other countries. If want to set own policies, it’s about economic opportunity. Don’t want to set up a regulatory framework where companies can’t operate here.
Question: As users, might we own our own data?
What your phone does, while you’re asleep. The amount of world knows about you isn’t good.
Trying to write a research proposal, going forward. How should we approach?
Panel not right format?
Closing, 1 minute each.
Great technology, fourth industrial revolution, will make changes, do have to approach with caution.
What is machine learning useful for, what isn’t it useful for? It’s mixed up.
AI, ML, data science — it’s the future, right conditions. Need to do more education of ourselves. There’s a lot we don’t know.
As you all know, R comes from S. But you might not know a lot about S (I don’t). This language used <- as an assignment operator. It’s partly because it was inspired by a language called APL, which also had this sign for assignment.
But why again? APL was designed on a specific keyboard, which had a key for<-:
Satire can be an antidote, says Prof. #PaulBabbitt@muleriders , to #bullshit (c.f. rhetoric; hypocrisy; crocodile tears; propaganda; intellectual dishonesty; politeness, etiquette and civility; commonsense and conventional wisdom; symbolic votes; platitudes and valence issues).
While lying is a misrepresentation of the truth, [Harry] Frankfurt argues, BS is an indifference to truth and a misrepresentation of the self—and worse than lying.
[…] BS is not only deceptive but also contributes to the decay of public discourse. Its emptiness, its meaninglessness crowd out substantive discussion. It directs attention to the trivial as much as the false, and it dumbs us down. Unlike the lie, BS derives its effectiveness from the way it says nothing while appearing to say something profound. […]
There’s no cure for BS, but there is a powerful treatment: satire, which can identify and mock BS, resistant as it is to conventional modes of argumentation and dispute. At its best, satire exposes the pretensions of the powerful. Irreverence sometimes troubles us, but irreverence, or at least the tension between reverence and irreverence, is essential to democracy. Reverence inspires an adherence to authority that is undemocratic at its core. In challenging authority, humor performs a critical democratic duty.
The short article in The Chronicle of Higher Education (November 2013) was preceded by a longer article for the American Political Science Association (August 2013).
If the opposite of bullshit is sincerity, then it may seem odd to offer satire as counter-measures. The mechanisms of exposing bullshit come close to bullshit itself. Satire is, after all, insincere. Stephen Colbert of the Colbert Report is a transparent persona that seems to have little to do with the actual person Stephen Colbert. Because it is transparent, though, it would be unfair to accuse Colbert of practicing bullshit in the same way that Callicles does. We may distinguish easily between the obvious and transparent performance of Stephen Colbert and the dissembling of a politician. […]
Humor may be mean spirited and cruel. However, to object to comedy because it is irreverent, because it challenges authority is to deny its most important power. It is precisely those modes that have the best chance of exposing bullshit to the ridicule it deserves. […]
There are good reasons to use such a strategy, and they can help us understand the purpose of satire in our own political environment. The fact that the satirist may pay with her life is perhaps the best evidence we have of its subversive quality. Satire sometimes provokes feelings of violation and violent reactions. Satire is irreverent, and they may target things you or I hold sacred. [….]
The only rule the bullshitter follows is to say anything so long as it works. As long as the bullshitter refuses to abide by standards of transparency and honest exchange of ideas then there is no choice but to engage the bullshitter on different ground. The satirist does not follow the same rules as the serious journalist or pundit. In the main, political humor is cast as critical, even destructive precisely because the humorist does not play by the same rules as “respectable” journalists. The rule breaking characteristic of the satirist is an important element in satire’s subversive character. It is not just that the satirist is targeting important powers of the system, it is that the satirist does not follow the rules either. [….]
There is of course an ugly side to this—a mass, largely uninformed audience may not be able to distinguish between exposing bullshit and mocking serious and sophisticated arguments—pomposity is to an extent a subjective evaluation of others. Comedy is indiscriminate in its targets. It not only poses questions, but it subjects its targets to ridicule. In its leveling, it erases distinctions and hierarchies that in fact are important elements in any human society. (McWilliams 1995) The informed and concerned public servant is as likely to be ridiculed as the most foolish politician. Comedy can and does expose the tendency of the public to follow the lowest common denominator. Furthermore, the kind of comedy most citizens will see, hear, or read has entertainment as its primary purpose. It does not escape the commercial imperative. One should suspect that if there is a choice between making money and performing a civic duty, money making will win out.
Babbitt, Paul. 2013a. “Bullshit, Politics, and the Democratic Power of Satire.” In American Political Science Association 2013 Annual Meeting. Chicago. https://ssrn.com/abstract=2301256.
Babbitt, Paul. 2013a. “Bullshit, Politics, and the Democratic Power of Satire.” In American Political Science Association 2013 Annual Meeting. Chicago. https://ssrn.com/abstract=2301256.
If we don’t first know “what is system is”, how do we approach an intervention? #MichaelCJackson OBE and Dr. #LuisGSambo appreciate the difference between “systems thinking” (plural) and “system dynamics” (singular), and suggest expanding theory with Critical #SystemThinking in Health Systems Research.
An ignorance of history is, if anything, even more pronounced among those authors in [Health Systems Research] influenced by complexity theory and the concept of ‘complex adaptive systems’. [….]
Most authors employing complexity theory in HSR seem to believe that it sprung forth fully formed from nothing or has somehow supplanted other bodies of work in systems thinking.
Such a poor appreciation of the history makes it almost inevitable that HSR will draw upon a restricted part of the systems and complexity tradition in developing its theories. In fact, it is the system dynamics and ‘complex adaptive systems’ strands that have come to dominate HSR at the expense of others. [….]
The problems start because of a lack of clarity in HSR about what type of ‘dynamic entity’, or ‘system’, a health system is. [….]
[Critical Systems Thinking] takes a radically different approach to HSR in the way it responds to the complexity encountered in the health systems domain. Primarily, HSR designates health systems as ‘complex adaptive systems’, and then looks to system dynamics to provide knowledge of their inner workings and supply insights into how they can best be managed. [….]
CST by contrast regards ‘messes’, like those found in public health, as ‘unknowable’. They give rise to what Rittel and Webber call ‘wicked problems’, which are intractable for decision-makers … [….]
CST, therefore, bypasses the issue of what kind of ‘system’ a health system is by stating that we will never know. Far from being a negative strategy, however, this opens up a whole new realm of possibilities, …
CST can assist … by referring to the three commitments of CST – ‘critical awareness’, ‘pluralism’, and ‘improvement’.
Source: Jackson, Michael C. & Sambo, Luis G. (2019). Health systems research and critical systems thinking: the case for partnership. 10.13140/RG.2.2.36160.48648.
In deciphering Yin-Yang and Five Elements (Five Phases) thinking, #Kaptchuk (1983) has a footnote and then an appendix that clarifies the way forward for appreciating foundations of Chinese medicine favouring the former. For philosophical correctness, Keekok Lee (2017) would frame the Chinese implicit logic as dyadic, rather than as a Western explicit logic of dialectic.
Yin (陰) and Yang (陽) Theory
The logic underlying Chinese medical theory — a logic which assumes that apart can be understood only in relation to the whole — can also be called synthetic or dialectical. In Chinese early naturalist and Taoist thought, this dialectical logic that explains relationships, pattern and change is called Yin-Yang theory. [†† ]
Kaptchuk (1983), p. 7
[††] Although the Chinese identify the relationships between phenomena primarily by the patterns of Yin and Yang, another system of categorization, known as the Five Phases, was also in use in early China. In this system, Wood, Fire, Earth, Metal and Water were seen as a set of emblems by which all things and events in the universe could be organized. Although the Five Phases categories permeate virtually every aspect of the traditional thought, leaving a significant impression on Chinese medical theory, this influence is for the most part formal and linguistic in nature. The Five Phases proved too mechanical, while Yin-Yang theory, because of its greater flexibility, was much for practical for the Chinese physician. It accommodated clinical changes and theoretical development that the tradition required in order to grow. (For a detailed discussion of the Five Phases in Chinese medicine, see Appendix H).
This important footnote seems to NOT show up the eBook versions for later editions that I’ve seen on the web (or maybe the previews are just incomplete).
Let’s jump down to Appendix H: The Five Phases (Wu Xing), with the note: This appendix was written in collaboration with Dan Bensky and the assistance of Kiiko Matsumoto. (We’ll skip over the preliminary Five Phases description, to get to discrepancies with Yin-Yang Theory).
The number five was important in the numerology of the period, particularly in for classifications of Earthly things. Various other numbers, such as six, four, and three, turn up in early classification schemes for things pertaining to Heaven. It is difficult to determine whether the importance of the number five led to Five Phases theory or the popularity of the Five Phases theory led to things being classified in fives.
Kaptchuk (1983), p. 346
 Jia De-dao, Concise History, pp. 29-30. For example, Lu’s Spring and Autumn Annals (246-237 B.C.E.) mentions Four Phases, omitting Earth.
During the third and fourth centuries B.C.E., the Five Phases theory and the Yin-Yang theory existed simultaneously and independently of each other. For example, Lao Tzu and Chuang Tzu refer extensively to Yin and Yang but do not mention the Five Phases. Unlike other traditional cultures with systems of elemental correspondences (e.g. the Greek Four Elements or the Hindu Three Doshas), the Chinese thus had two systems of referents. It was not until the Han dynasty, a period of great eclecticism and synthesis, that the two systems began in merge in Chinese medicine. “The five elements [Phases][which] had not been part of the most ancient Chinese medical speculations” were incorporated into the clinical tradition that culminated in the Nei Jing. Certain parts of the Nei Jing refer to the Five Phases, whlle others do not. Yet other texts, such as the Discussion of Cold-Induced Disorders and the biography of Bian Que in the Shi Ji or Historical Records, make no mention whatsoever of Five Phases theory. The Five Phases theory continued to undergo changes even after its incorporation into Chinese medicine. It is not until the Song dynasty (96-1279 C.E.) that the relationships between the Phases were commonly used to explain the etiology and processes of illness.
Kaptchuk (1983), p. 346
 Fung Yu-lan, History of Chinese Philosophy, vol 1, p. 8; Chan, Chinese Philosophy, p. 224; Hans Agren, “Patterns of Traditional and Modernization in Contemporary Chinese Medicine,” in Medicine in Chinese Cultures: Comparative Studies of Health Care in Chinese and other Societies, ed. by Arthur Kleinman, et al. (Washington, D.C.: John E. Fogarty International Center, U.s. Dept. of HEW, NIH, 1975), p. 38
 Lu Gwei-djen and Joseph Needham, “Records of Diseases in Ancient China,” American Journal of Chinese Medicine 4, no. 1 (1976): 12.
 Dan Bensky, “The Biography of Bian Que in the Shi Ji,” unpublished manuscript, University of Michigan, 1978, p. 2.
 Recent archeological discoveries of pre-Nei Jing texts confirm the impression that Yin-Yang was originally a much more important port of Chinese medicine than the Five Phases theory. See “A Simple Introduction to Four Ancient Lost Medical Texts Found at the Tomb of Ma-wang,” Medical History Text Research Group of the Academy of Traditional Medicine, Wen Wu, no. 6 (1975), pp. 16-19. The Five Phases are not mentioned in these ancient medical writings. See Chapter 4, Note 3.
 Jia De-dao, Concise History, pp. 165-166.
Many attempts were made to fit the Five Phases neatly into the Yin-Yang structure. For example, Wood and Fire were considered the Yang Phases, being active in character, while Metal and Water, associated with quiescent functions, were the Yin Phases. Earth was the balance point between Yin and Yang, despite this apparently successful marriage between Five Phases and Ying-Yang theory, the two systems of correspondence frequently yielded different interpretations of health and disease 
For example, Five Phases theory might emphasize the following correspondences stated in the Nei Jing: The Liver opens into the eyes; the Kidney opens into the ears; the Heart opens into the tongue. Disorder in a particular orifice would necessarily be linked into is corresponding Organ.
Yin-Yang theory, on the other hand, might emphasize the the following quite different assertions of the Nei Jing: The pure Qi of all Organs is reflected in the eyes; all the Meridians meet in the ears; the tongue is connected to most of the Meridians. Yin-Yang theory would not necessarily see a link between a part and a part. Rather, all disharmonies of the eyes, ears or tongue would be interpreted in terms of patterns. Thus, an eye disorder could be part of a Liver disharmony or perhaps a Lung or Spleen disharmony, depending on the configuration of other signs.
The differences between these medical interpretations stem from the fact that Five Phases theory emphasizes one-to-one correspondences, while Yin-Yang theory emphasizes the need to understand the overall configuration upon which the part depends. And so, although Five Phases theory is ideologically more dynamic than, for instance, the Greek or Hindu systems, and is actually being applied creatively to medical practice, it became a rigid system. Yin-Yang theory, on the other hand, with its emphasis on change and view of the importance of the whole, allowed for a great deal of flexibility. It was therefore easier to adapt to the needs of clinical practice.
Kaptchuk (1983), p. 346-347, editoral paragraphing added.
 Porket, Theoretical Foundations, p. 118. “Traditional Chinese thought has a general tendency to reconcile and harmonize different or even mutually exclusive ideas in an arbitrary syncretism. Contrary doctrines — for instance, Nakamura’s discussion of this Chinese characteristic states: “What stand out in this sort of reasoning is a certain sort of utilitarianism and early compromise with cold logical considerations completely abandoned.” Hajime Nakamura, Ways of Thinking of Eastern Peoples (Honolulu, East-West Center Press, 1968), p. 291.
In that last paragraph, “Yin-Yang theory emphasizes the need to understand the overall configuration upon which the part depends” could be interpreted either as a systems approach, or as a context for the dyadic. Five Phases theory appreciates part-part interactions, but may miss the whole (that is foundational to systems thinking).
Chinese medicine has had to take many liberties with the Five Phases theory to fit it to actual medical experience. The physiology that grew out of Five Phases theory, for example, is not identical with traditional Chinese physiology. The tradition is based on empirical observation and is ultimately connected to Yin-Yang theory, concentrating on the functions of the Organs and extrapolating their interrelationships from their functions. The Organs are thus the key to the system. Five Phases theory does not always agree with this understanding, and in that case, it is simply ignored. For example, in Five Phases physiology, the Heart corresponds to Fire. Traditional texts, however, consider the Kidneys (Life Gate Fire) to be the physiological basis for the Fire (Yang) of the other Organs. And so, the Five theory’s formal correspondence would be conveniently forgotten.
Kaptchuk (1983), p. 347
 Qin Bo-wei, Medical Lecture Notes , pp. 15-22.
We’ll skip over the “Use of the Five Phases in Medicine” (pp. 347-351), towards favouring Yin-Yang theory.
Criticism of Five Phases Theory
The Five Phases theory has been the subject of criticism ever since its invention. The challenges to its veracity and practicality date as far back as Mohist contemporaries of Zou Yen (fourth century B.C.E.). For example, one comment on the Mutual Control order reads: “Quite apart (from any cycle) Fire melts Metal, if there is enough Fire. Or Metal may pulverize a burning fire, if there is enough Metal. Metal will store Water (but does not produce it). Fire attaches itself to Wood (but is not produced from it).”
Kaptchuk (1983), p. 351
 Quoted in Needham, Science and Civilization, vol. 2, pp. 259-260.
A few hundred years later, the great Han dynasty scientist and skeptic Wang Cong satirized the results of literal application of the Five Phases theory. Here are two short excerpts from his work:
Kaptchuk (1983), p. 351
The body of a man harbors the Qi of the Five Phases, and therefore (so it is said) he practices the Five Virtues, which are the Tao (Way) of the Phases. So long as he has the five inner Organs within his body, the Qi of the Five Phases are in order. Yet according to the theory, animals prey upon and destroy one another because they embody the several Qi of the Five Phases; therefore the body of a man with the five inner Organs within it ought to be the scene of internecine strife, and the heart of a man living a righteous life be lacerated with discord. But where is there any proof that the Phases do fight and harm each other, or that animals overcome one another in accordance with this?
The horse is connected with the sign wu (Fire); the rat with sign zi (Water). If Water really controls Fire, (it would be more convincing if) rats normally attacked horses and drove them away.
Wang Cong, cited in Kaptchuk (1983), p. 352
 Ibid., pp. 265-266. Translation altered by author.
Despite such early criticism, the Five Phases theory became entrenced in Chinese medicine. One reason for this is that Chinese investigative study tends to be inductive only to a point and then proceeds with deductions based on classics. The Five Phases theory thus served as an orthodox reference for numerous speculative deductions.. Most modern Chinese critics describe Five Phases theory as a rigid metaphysical overlay on the practical and and flexible observations of Chinese medicine.
Kaptchuk (1983), p. 352
 Nakamura, Ways of Thinking of Eastern Peoples, p. 190.
Another major criticism, and a primary difﬁcu!ty in the application of the Five Phases theory to medicine, is its lack of consistency. To fit the theory to reality, the referents of the Phases and the relationships between them haye continually been changed and corrupted. The results of such corruption cap be seen in Tables 74 and 75 on the clinical use of the Five Phases.
Such a problem exists in all traditional systems of elemental correspondence. The original classical Greek formulation by Empedocles of Agrigentum (c. 504-433 B.C.E.) is a system in which the basic elements of fire, earth, water, and air were considered the ultimate constituents of matter and were associated with various other categories of four such as the four fundamental qualities and the four humors. All varieties and changes in the world were associated with different mixtures of the four elements. [….]
Kaptchuk (1983), p. 352
 To get a sense of the cultural, physiological, scientific, ideological, religious, and intellectual factors that are involved in a correspondence system, it is worth examining the transition from the Aristotelian system of Four Elements to the Paracelsian Three Elements (tria prima: salt, sulphur and mercury) in sixteenth-century Europe. an interesting discussion appears in Allen G. Dobus, “The Medico-Chemical World of the Paracelsians,”, in Changing Perspectives in the History of Science, ed. by Mikaluas Teich and Robert Young (Dordrecht, Holland, and Boston: D. Reidel Pub. Co., 1973), pp. 88-92
Western practitioners of acupuncture and Chinese medicine have special problems dealing with the Five Phases theory. The major difficulty is that much of the literature available in English describes diagnosis and treatment exclusively in terms of Five Phases theory. Writings that refer to the theory as the “Law of the Five Elements” betray a misunderstanding of Chinese science—natural laws such as those promulgated by Aristotle and Newton simply were not developed in traditional China. These writings also put undue emphasis on the importance of the Five Phases to the Chinese medical tradition; even respected defenders of the Five Phases theory readily admit sometimnes it is useful and sometimes it is not. Even so, it is unfortunate many practitioners simply consider Five Phases theory unscientific gibberish, and do not try to understand it. It is actually an important secondary emblem system used to assess and discuss clinical reality.
 An example is Denis and Joyce Lawson-wood, The Five Elements of Chinese Acupuncture and Massage (Rustington, England: Health Science Press, 1965). The English overemphasis on the Five Phases is not derived from the Chinese tradition. Instead, the fascination of European acupuncturists with this method is due to the influence of the “Nan Jing traditional acupuncture movement” and to to somem of the Kei Raku Khi-Riyo (Meridian Treatment) schools, both of which developed around the turn of the twentieth century, in Japan. The European adoption of this method stems partly from a desire for an exotic schema and partly from lack of adequate information.
 See Needham’s discussion of Chinese thought and “law” in Grand Titration, pp. 299-330.
 Qin Bo-wei, Medical Lecture Notes, p. 22.
 An example is Frank Z. Warren, Handbook of Medical Acupuncture (New York: Van Nostrand Reinhold Co., 1976).
Kaptchuk, Ted J. 1983. The Web That Has No Weaver: Understanding Chinese Medicine. Chicago: Congdon & Weed.