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Introduction
When it comes to your deepest, darkest nature, there’s a whole lot of accusing going on. The fight or flight folks tell you that your biology comes complete with an overwhelming instinct for survival. Rational-choice-obsessed specialists like economists and evolutionary psychologists claim that you are not only preprogrammed for survival at all costs, but that you are wired to maximize your personal gains. And evolutionary biologists like William Hamilton, Richard Dawkins and Robert Trivers assert that you are biologically programmed to maximize the spread of your selfish genes.
These claims about your most intimate nature are concealed in the very foundations of many of the social and evolutionary sciences, from evolutionary biology and evolutionary psychology to political science, sociology, philosophy, and economics. They are buried deep below the surface in the form of assumptions—often hidden assumptions. But these assumptions about your innermost self are only half-right. More important, they are half-wrong. And the half that’s wrong throws evolutionary and social sciences way off base. Which may explain why one of the most lauded and mathematically savvy economists of his day, Irving Fisher, told the New York Times in mid-October, 1929, that stocks were about to go “a good deal higher within a few months.” Then on October 29th came Black Tuesday and the great crash of 1929.
Like the economy, your biology often refuses to follow the paths that scientific assumptions about survival and selfishness would predict. Yes, some of your internal mechanisms do work to stack up your survival advantages. And to maximize your sexual prizes. But you are also plagued with internal mechanisms that do the very opposite, biological mechanisms that hobble you: depression, anxiety, brain freeze, and even impulses toward suicide. What’s more, on occasion you are willing to sacrifice your life for something higher than yourself. Ten million followed that inner call in the First World War.
Your self-destruct mechanisms—human self-destruct mechanisms--are not unique. They’ve been integral to the 3.85-billion-year history of life. They are similar to apoptotic mechanisms, the “programmed cell death” mechanisms, that individual cells in a bacterial society or in your body use to commit suicide.
Why do self-destruct mechanisms of this sort exist? Because the believers in the survival instinct, the rational choice model, and the selfish gene miss something. Something big. Your biology is not wired strictly for personal gain. It is wired to make you a component in something far greater than yourself. It is wired to make you a module in a collective learning machine, a complex adaptive system, a parallel-processing search engine, a group IQ, a social intelligence. You are wired to be a neuron in a social brain.
A neuron in a brain that, like you, collaborates to make something bigger than itself: a global brain.
Methods
This talk is based on the research for my first two books, The Lucifer Principle: A Scientific Expedition into the Forces of History and Global Brain: The Evolution of Mass Mind from the Big Bang to the 21st Century. That research drew heavily on work in evolutionary biology, evolutionary psychology, artificial intelligence, animal behavior, experimental psychology, neuroscience, psychoneuroimmunology, anthropology, and history. It involved the use of over 4,000 reference sources.
Results and Discussion
The common wisdom—and the accepted view in evolutionary biology—is that we humans and our animal cousins are built with a survival instinct. An instinct that watches out for our personal survival, and for personal survival ahead of everything else.
Yet when we encounter a saber tooth tiger on a pathway, we have three modes of response: not just Walter Bradford Cannon’s fight or flight, but fight, flight, or freeze. And freeze can be suicide. That’s not the only suicidal mechanism locked into our biology. When you’re about to take that all-important math test and you’ve studied for weeks, then finally open the test booklet, your mind locks up. It refuses to remember a thing. That anxiety blackout is not a product of a patriarchal civilization, industrialism, or capitalism. It is a product of your biology.
When you are fired or your wife tells you she is leaving you, your survival instinct should put your mind into overdrive. It should give you the juice to hunt with energy and creativity for your next step. But high-power cognitive processing is not what your biology serves up. Instead, it bogs you in depression, a state in which it’s hard to think of even tying your shoes.
How do mental paralysis and depression up your odds of survival? They don’t.
So what gives? How could natural selection possibly favor such obvious self-destruct mechanisms? The answer is in the algorithms that power collective intelligences, mass learning machines like neural nets and the immune system. Rules that turn down the flow of resources and influence to the nodes that fail to cope with the problems of the moment and turn up the resource-flow and the influence of the nodes that have a handle on things.
Conclusions
We need a radical overhaul of the assumptions underlying many of our sciences and many of our pop-cultural beliefs. Yes, we need to recognize our survival instincts and our selfish biological built-ins. But we also need to see our role in something higher than ourselves—complex adaptive systems, neural-net-like collective learning machines. If we do, many of the “irrational” mysteries that make the predictions of so many social scientists wrong may suddenly appear to be rational after all.
References and Notes
- Bloom, H. The Lucifer Principle: A Scientific Expedition into the Forces of History; Atlantic Monthly Press: New York, USA, 1995.
- Bloom, H. Global Brain: The Evolution of Mass Mind from the Big Bang to the 21st Century; John Wiley & Sons: New York, USA, 2000.
- Bredesen, D. Neural Apoptosis. Annals of Neurology 1995, Vol 38, 839–851.
- Cannon, W.B. Bodily Changes in Pain, Hunger, Fear and Rage: An Account of Recent Researches into the Function of Emotional Excitement; D. Appleton: New York, USA, 1915; pp. 202, 275-277.
- Dawkins, R. The Selfish Gene; Oxford University Press, New York, USA, 1976.
- Durkheim, E.; Suicide: A Study In Sociology; translated by John A. Spaulding and George Simpson. The Free Press: New York, USA, 1951 (originally published 1897).
- Haanen, C., Vermes, I. Apoptosis: programmed cell death in fetal development. European Journal of Obstetrics, Gynecology, and Reproductive Biology 1996, Volume 64, Issue 1, 129–133.
- Harlow, H.F.; Suomi, S.J. "Production and Alleviation of Depressive Behaviors in Monkeys." In Psychopathology: Experimental Models; Maser, J.D.; Seligman, M. Eds.; W.H. Freeman and Company: San Francisco, USA, 1977; pp. 167-170.
- Holland, J.H. Hidden Order: How Adaptation Builds Complexity; New York, USA: Addison-Wesley, 1995.
- Hopfield, J.J. "Neural networks and physical systems with emergent collective computational abilities." Proceedings of the National Academy of Sciences of the United States of America 1982, Vol. 79, Number 8, 2554-8.
- Kirschner, M.; Gerhart, J. Evolvability. Proceedings of the National Academy of Sciences of the United States 1998, vol. 95 no. 15, 8420–8427.
- McClelland, J.L., Rumelhart, D.E. and the PDP Research Group; Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 2: Psychological and Biological Models; A Bradford Book, The MIT Press: Cambridge, MA, USA, 1986.
- Miller, W.R.; Rosellini, R.A.; Seligman, M. "Learned Helplessness and Depression." In Psychopathology: Experimental Models, Maser, J.D., Seligman, M., Eds.; W.H. Freeman and Company, San Francisco, USA, 1977; pp. 104-130.
- Nasar, S. Grand Pursuit: The Story of Economic Genius; Simon & Schuster: New York, USA, 2011; pp. 312-313.
- Kevin A. Roth, K.A, D'Sa, C. Apoptosis and brain development, Mental Retardation and Developmental Disabilities Research Reviews 2001. Volume 7, Issue 4, 261–266.
- Tomei, D., Cope, F.O., editors, Apoptosis: the molecular basis of cell death. Cold Spring Harbor Laboratory Press: Plainview, NY, USA, 1991.
- Wilson, E.O. Sociobiology: The New Synthesis. Harvard University Press: Cambridge, MA, USA, 1975.
- Wilson, E.O. The Social Conquest of Earth, W.W. Norton: New York, USA, 2012.
- Wilson, D.S. Does Altruism Exist?: Culture, Genes, and the Welfare of Others (Foundational Questions in Science). Yale University Press: New Haven, Connecticut, USA; 2015.
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Introduction
China has a long-standing problem for patients to queue and book appointments with doctors (gua hao) in real life, especially with those medical experts (zhuan jia hao). The demands for expert doctors in 3AAA hospitals are extremely high, leading to a hidden market for scalpers to trade doctors’ appointment notes. To tackle this problem, China introduced a series of regulations in the year of 2009 as a means of healthcare reform, and required these 3AAA hospitals to adopt the Online Booking Systems (wang shang gua hao; abbreviated as OBS below) gradually – in which patients can book with doctors in advance by their personal identity information and doctors can easily access his/her clinical record before the appointment [1]. This is quite similar to GP online services offered by NHS (i.e. www.chooseandbook.nhs.uk); but it can be both managed by public institutions such as Beijing Health Bureau (www.bjguahao.gov.cn) and private funds (http://www.guahao.com/). This paper aims to examine how these online booking systems can transform patient experience.
Methods
This study interviews five groups of middle-aged people (with 3 – 5 people each group) in Beijing, mainly focusing on their experience of booking appointments with doctors as well as those of their relatives/friends [2]. A set of semi-structured questions are asked to identify the extent to which the OBS have changed patients’ behaviors/perceptions, as well as to examine what factors have constrained their adoption of OBS [3]. Meanwhile, this study will assess the impact of technological change in relation to various socio-economic factors [4]. Throughout this process, participants will be asked to identify problems needing to be the most urgently tackled, regarding China’s healthcare reform. Lastly, their views towards electronic medical records are investigated in relation to the privacy issue.
Results and Discussion
In overall, participants interviewed by this study have shown positive attitudes towards the Online Booking Systems (OBS), mainly due to its accessible feature – i.e. People can take the initiative in terms of scheduling their own appointments (Besides this, many of them also use the telephone platform “114” as an alternative to make appointments). But it is clear that numbers of appointments allocated to OBS are limited [5]. Moreover, some criticize these technological advancements as a “temporary medical relief that only treat the symptom”, given the fact that 1) the supply of medical resources – still unevenly distributed both at the national and regional level – cannot meet the demand of patients and 2) the scarcity of expert doctors exacerbate patients’ willingness to strive for the perceived “best” medical service – despite few complained about the quality of these experts. On this basis, factors such as “illness seriousness”, “emergency extent” (mainly means those needing operations) etc. could pressure patients or their relatives to buy expert doctors’ appointment notes from scalpers for higher prices. Regarding the electronic medical records, most participants interviewed in this group have shown some extent of agreement on sharing them with their doctors as well as for further medical research; while they strongly opposed letting third parties to use them for commercial purposes (e.g. recommending medicine).
References and Notes
- Li, Q.; Li, W. Large Hospital Registration System Design and Implementation. Programmable Controller & Factory Automation 2011, 9, 51-55.
- The middle-aged group always needs to take care of elderly people and children, thus having extensive experience in terms of making appointments with doctors.
- Bryman, A. Social Research Methods, 4th ed.; Oxford University Press: Oxford, 2012.
- Kierkegaard, K. Governance Structures Impact on eHealth, Health Policy and Technology 2015, 4, 39-46.
- Liu, B.; Jiang, X.; Cao, D.; Wang, L.; Wang, H. A Comparative Study of Different Booking
- Methods in Hospitals of China, Modern Hospital Management 2007, 5, 5-7.
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Introduction
Objectivism, supposing that information is ‘out there’ and can be accessed through appropriate research methods, is a valuable and unavoidable initial stance in field work. However, even within an objectivist paradigm, information gathered from field work can never be accepted uncritically, however rigorous the research methodology, since each step of the process from the choice of methodology onwards is driven and circumscribed by the values and beliefs of the participants.
In response to the growing threat of climatic change researchers are increasingly utilising social surveys to access information on human-environment interactions or the operation of “social-ecological” systems, in order to preserve key functions into the future. This paper explores the sources of uncertainty which emerge as simple environmental data transfers from participant to researcher. In particular, it considers the role that values can play in determining the quality of participant-reported quantitative environmental data, presented within the framework of Shannon’s standard communication model.
Gathering information from interviews and surveys
Survey participants have a theory of the world. The knowledge they possess is dependent on this theory [1]. In the philosophy of information, the information exists in a specific level of abstract [2]. Participants’ theories encompass the values – ethical, political, social, and religious – they have developed in their life and work.
The researcher has a different theory of the world. He/she too comes with a framework equally embedded within a set of values, different from those of the participants.
In qualitative research, an interpretative stance recognises the context-dependence of knowledge:
“Interpretive methods of research start from the position that our knowledge of reality, including the domain of human action, is a social construction by human actors and that this applies equally to researchers. Thus there is no objective reality which can be discovered by researchers and replicated by others, in contrast to the assumptions of positivist science” [3]
What about accessing numbers through interviews and surveys? If an interviewed farmer tells a researcher “my yield was 8.5 tonnes”, what is the status of the “8.5 tonnes”?
To explore the status of apparently simple numerical data we model the data acquisition using the standard communication model due to Shannon [4], consisting of: a message source; encoder; noisy channel; decoder and message destination. Although the validity of using the Shannon model outside its origin in the engineering of telecommunication systems has been contested (see for example [5] and [6]), it provides a convenient structure to explore issues with research data which would need to be addressed, whatever the model.
The supposed ‘perfect’ data exists as the message generated at the source, and we explore what becomes of this message as it travels to the destination where the data is embedded in the work of the researcher.
- Issues with the source (the participants) and the destination (the researcher), including a mismatch between their respective theoretical frameworks, so that the numbers in one can never have the same meaning in the other.
- Issues with the encoding (framing, writing and talking) and decoding (extracting, reading, listening), including the use of different languages, both metaphorically and literally.
- The channel is unreliable.
Our case study is a field study conducted in the Vietnamese portion of the Mekong Delta, which aimed at quantifying the benefits local farmers receive from an environmental service, the deposition of nutrient-rich fluvial sediments during the annual monsoon.
A case study: rice farmers in Vietnam
Accessing quantitative, environmental, data through social surveys has, as a methodology, seen rapid growth in recent years, particularly as a result of approaches such as the Sustainable Livelihoods Framework [7] which are used to evaluate the success of human adaptations to environmental change (seem [8], [9] and [10]) by measuring the capital - natural, human, social, and economic - at the disposal of the participant. The work considered here was an expedition to Vietnam aiming to access information on the impact of climate change and changing farming practices on the farmers of the Mekong Delta. In April and May 2014 a native English speaking researcher teamed up with five native Vietnamese speaking researchers to conduct 434 interviews spread across 19 villages and two provinces of the Delta. The interviews were conducted in Vietnamese, with the enumerator asking closed quantitative questions, recording the response in Vietnamese, and later translating and converting it into digital format, before forwarding it to the English-speaking researcher for processing.
Among the information sought from the interviews were: farm size, amount of fertilizer used; depth of sediment following the annual flooding; and crop yields.
We can explore where and how the numbers degrade at each stage of the communication model.
1 Issues with the source (the farmer). The farmer might be thinking of the wrong number for various reasons.
- Estimation factually inaccurate due to misperceiving/mismeasuring quantity at first attempt (e.g. farmers do not have accurate weight measuring instruments, and the rice they measure will not be pure)
- Thinking of the wrong number due to misunderstanding the question. The interviewers were asking questions using technical language common to an academic environment. In some cases farmers would not have been sufficiently familiar with this language and may have purely misunderstood what they were being asked.
- Estimation warped by exaggeration of the past (e.g. when farmers remember rates of change which have negatively affected them they tend to exaggerate when their answers are compared with the government’s own data (which of course could be wrong))
2 Issues with coding. The farmer might know the correct number but report it incorrectly for various reasons.
- deliberately increased to ‘save face’ in front of peers
- deliberately decreased to gain additional ‘handouts’ from the government
- deliberately altered to impress researcher
- deliberately altered because they think they know what answer the researchers ‘really wants’
- deliberately altered to serve a personal agenda they are pursuing
- deliberately made controversial to enhance self-importance
- deliberately altered because the participant doesn’t like researcher
3 Noise in the communication channel. The farmer might report the right number but the researcher might receive it incorrectly for various reasons such as mishearing an answer, errors in transcription (a common issue was that survey enumerators would write the answer in the wrong answer box, or not in a box at all), or errors in translation
4 Issues with the decoding or mismatch between the coding/decoding including misunderstanding units. For example, two different units of land area are used, both called Cong. One is the ‘new’ 1000m2 and the other is the ‘old’ 1300m2 but both are still in use. Also, misunderstanding of what the number signifies. For example, some farmers reported rice in dry weight, as opposed to wet, which substantially reduces the number.
5 Issues with the destination (the researcher). For example, asking the wrong person and/or at the wrong location – perhaps made a mistake when sampling, or just poor understanding of the issue being investigated.
Influence of the location of the interviews
In this section we go in-depth into one error formed during the farmer’s coding process. A challenge of overseas fieldwork is that it is not always possible to maintain full control over the execution of the project, and this is particularly the case in Vietnam where the political context can have a strong influence. The initial aim of this fieldwork project was to run all farmer interviews individually at the farmer’s homestead however, due to preferences of the local authority, more than half the interviews were ultimately conducted in large groups. The group setting meant farmers were subject to greater peer scrutiny while reporting their data, but perhaps less governmental scrutiny. This introduced a new potential avenue for the farmer’s values to create errors in their reported data.
In order to probe further into the impact of these particular values, some statistical analysis was conducted on the reported data. After controlling for some key factors affecting the yield achieved by rice farmers we found that the binary variable of either an individual or group setting had a significant (p<0.01) correlation with yield, with the group setting increasing the mean yield reported by 0.26 (± 0.13) tonnes (around 3%).
The role of values
While at first sight, at least, some of the sources of error are accidental (such as mishearing a number), many are also a consequence of value.
The extent to which such sources of error affect the farmer’s reported data depends on the difference between what he/she values compared to the researcher values: do they prioritise: honesty/scientific advancement (an ethical concern), fear of authority (political), personal feelings towards the researcher (including racial biases), general pride, or social standing (social).
The difference in values reported in home interviews compared to group discussions demonstrates a second-order impact of values: the data reported by the farmer depends on the farmer’s perception of the values of the witnesses.
Almost all of the researcher’s errors can be reduced through increasing the time/diligence with which the interviews are conducted. Hence, the magnitude of the error on the researcher’s side is dependent on what they value more: accuracy of the data (less interviews but more accurate data) which ensures work will stand up to scrutiny of peers, or statistical operability (more interviews but less accurate data) which may affect whether the work is publishable.
Discussion
Numbers are never value-free. Numbers only have meaning by virtue of their context, and the decision to select one parameter over another already entails implicit or explicit value judgments. The decision to investigate rice farming in the Mekong Delta was embedded in a set of values of the geography department of the University of Southampton, itself embedded in the values of several wider communities. The researcher then identifies the need for numbers, but these numbers have meaning within the abstraction of the research. They do not have the same meaning in the minds of the farmers.
This paper attempts to explore the consequences of the fact that the numbers are embedded in values. Given that the research requires specific data (amount of fertilizer used; depth of sediment; crop yields), it is suggested that the data acquired is unavoidably influenced by the values of the farmers and the society in which they live and work, by the values of the research team, and by the interaction of values of all the actors involved.
In the present context of intensifying environmental change, the use of numbers reporting on human-environment interactions accessed through quantitative surveys is only likely to increase as a fast and cost-efficient method of garnering information covering large geographical regions and populations. Furthermore those numbers are likely to be utilized by policy makers designing large-scale, expensive and often irreversible, hard and soft interventions in social-ecological systems (‘adaptations’). An appreciation of the effect of values on the reliability of the numbers (information) being examined is essential, to avoid catastrophic ‘maladaptations’. The communication model implemented above provides a valuable framework, or lens, through which to view the potential sources of bias brought about by values and indeed other factors during the number collection process.
Acknowledgments
The authors wish to thank the staff and students at Can Tho University, Vietnam for their support in the field.
References and Notes
- Chalmers, A. F. (1980, 2nd ed. 1982), What is this thing called Science?, The Open University Press, Milton Keynes.
- Floridi, L (2013) “The Ethics of Information”, OUP, Oxford
- Walsham, G. (1993), Interpreting Information Systems in Organizations, Wiley, Chichester, UK.
- Shannon, C. E. (1948), 'A Mathematical Theory of Communication', The Bell System Technical Journal 27, 379–423, 623–656.
- Reddy, M. J. (1979), The Conduit Metaphor - A Case of Frame Conflict in Our Language about Language, in Andrew Ortony, ed., 'Metaphor and Thought', CUP, Cambridge.
- Chapman, D. A. (2014), 'Information is Provisional', Kybernetes 43, 895-910.
- Scoones, I. (1998). Sustainable Rural Livelihoods: A Framework for Analysis (p. 22). Institute of Development Studies, Brighton.
- Holler, J. (2014). Is Sustainable Adaptation Possible? Determinants of Adaptation on Mount Kilimanjaro. The Professional Geographer 66, 526–537.
- Suckall, N., Tompkins, E., & Stringer, L. (2014). Identifying trade-offs between adaptation, mitigation and development in community responses to climate and socio-economic stresses: Evidence from Zanzibar, Tanzania. Applied Geography 46, 111–121.
- Thi, M. H. B., & Schreinemachers, P. (2011). Resettling Farm Households in Northwestern Vietnam: Livelihood Change and Adaptation. International Journal of Water Resources Development 27, 769–785.
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