So for those of you who don’t know, we recently picked out a dachshund puppy, who came to live with us today. We’ll try not to be those people who take way too many pictures of the puppy, but to be honest, right now he’s too cute not to take pictures of. So without further ado…
Remember that book I was reading a long time ago? Evangelism After Christendom? Yeah. It’s back thanks to a Kindle edition.
When we left Stone, he was attempting to give us the idea that in the Christian tradition, evangelism could possibly be viewed as a core practice in a loosely Macintyrian sense. Stone also takes some time to point out core problems with the way evangelism is often executed in modern churches. Chief among these problems, he argues, is that evangelism has become essentially a marketing regime which seeks to attract new people by either a) trying to make the gospel more intellectually respectable b) trying to demonstrate that it is practical (good for society, economy, or personal psychology), or c) attempting to alter the traditional “stuffiness” that has categorized church in the past and instead make church more accessible to a wider audience. Stone:
Creative reconstructions of evangelism are being attempted today, and they succeed in expanding the church by adapting it to new generations that are put off by boring liturgies, irrelevant preaching, and stuffy pipe-organ music. But while these reconstructions have triumphed in making the church more relevant to the tastes, expectations, preferences, and quest for self-fulfillment of both the unchurched and the dechurched, they have utterly failed to challenge the racism, individualism, violence, and affluence of Western culture. They in no way subvert an existing unjust order but rather mimic and sustain it. Our greatest challenge is to find ways of practicing evangelism in a post-Christendom culture without at the same time playing by the rules of that culture.
Cliff’s notes? Marketing evangelism works – at least if what you mean by “works” is “attract more people”, but it doesn’t do a terribly good job of remaining true to the Christian ethos, which if you will remember from our first discussion, is what really matters. Stone again:
We kid ourselves if we think we have moved beyond Christendom simply because we are able to reach more people by getting rid of our stained glass and stuffy sermons and providing a “product” that is more user-friendly. Neither large-scale revivals that boast thousands of converts nor fast-growing megachurches that have dropped from the sky into suburban parking lots as of late are in any way indications of the proximity of God’s reign, nor is their winsomeness and friendliness to be equated with Isaiah’s “peace.” In fact, the failure of evangelism in our time is implied as much by the vigorous “success” of some churches in North America as by the steady decline of others.
This is, I think, a profound statement. You may recall a recent post where we talked about the metrics we use to evaluate whether God is “working.” What is true on an individual level is also in many ways true for Christianity as a collective – namely that we tend to view God “working” in rather selfish terms – specifically when it looks like our agenda is “winning”, our political candidates are getting elected, and our numbers are increasing. There are no shortage of problems with this theology, as pointed out in the previous post, but Stone adds another: by using metrics of success that are external to the practice, we are essentially distorting and subverting the practice itself and trading excellence for sheer effectiveness, and indeed by confusing the two. Returning to the oft-used analogy of sports, effectiveness is winning a championship – excellence is playing to your highest potential day in and day out, letting the results speak for themselves. Ted Williams is considered to be one of the finest hitters to ever play the game of baseball, but he never won a World Series. You don’t necessarily have to be excellent to be effective – in fact, being effective can be achieved in plenty of ways contrary to the ethos (ideals) or telos (purpose) of the tradition you find yourself apart of.
One way Stone proposes that we counter this tendency is to first ground evangelism theologically, rather than allowing it to be whatever it wants in order to be successful.
Those who think theologically rarely think about evangelism, and those who think about evangelism rarely take the discipline of theology very seriously. For one thing, very little in the present reward system of most churches supports thinking theologically about evangelism. Excellence in evangelism is almost wholly governed by numerical measures of success, and pastors are rewarded primarily insofar as they attain those measures. Those who produce the literature on evangelism – especially that which concentrates on the models that are widely touted as successful in the North American context – are particularly reluctant to think critically about the theology presupposed in their practice. Their focus instead is on finding new and creative ways to express Christian beliefs and practices – forms that are more indigenous, user-friendly, and “relevant” to the experience of contemporary human beings, or more successful in making converts in an already crowded marketplace of competitors.
This book is written out of the conviction that there is no substitute for serious theological inquiry about evangelism as a practice. In fact, theological inquiry is itself an intrinsic part of that practice. We cannot proceed by merely trotting out a handful of “successful” pastors of fast-growing congregations to tell us what “works”. For it is the very question of what we are working toward, what is deemed valuable and beautiful, what we are seeking, that in our time must be reexamined and that too often goes unchallenged altogether.
The “practicality of theology does not lie merely in its strategic movement toward concrete proposals for action. Practical theology is not a bag of tricks, but a process of laying bare the assumptions that guide our practice and then drawing critically upon the practical wisdom of Scripture and the Christian tradition in order to rethink and reconstruct those assumptions.
Stone’s conclusion? Evangelism isn’t about trying to translate the message we think we know into a new context, but about residing in a changing context and remaining (or becoming) faithful witnesses of God’s peace. This is not about setting up an alternate culture that never interacts with the world around it. It is not a culture that is different because it shuns sex, drugs and rock and roll, but because it challenges, in the case of our current position, the very foundations of modern society – the economic, social and political power structures that so often serve as today’s “powers and principalities of this dark world”. Evangelism, for Stone, is primarily about remaining grounded in a life of faithful dedication to the ethos of the Christian tradition – in his words, “witness to God’s reign of peace”.
When the practice of evangelism is not grounded firmly in the comprehensive life of witness, the church is inevitably instrumentalized, reduced to a mere tool in the service of heralding the gospel, rather than the social embodiment of God’s new creation in Christ, the very news that is to be heralded as good. For, as always, the embodiment is the heralding; the medium is the message; incarnation is invitation. That is why, as I shall attempt to argue throughout this book, it is impossible for the church to evangelize the world and, at the same time, to serve as a chaplain to the state and allow itself to be disciplined by the logic if the market.
There are some real issues in that statement – issues that challenge the predominant theology (primarily soteriology and eschatology) in some deep and profound ways. My personal belief is that most people are not ready for the type of change that Stone is outlining, but that it might be possible to move things slowly in that direction.
This weekend I finished reading David Orrell’s book “The Future of Everything: The Science of Prediction“. As an applied scientist, the public perception of scientific modeling has been a side interest of mine. In particular, as science is pressed more and more into the service of politics and ideology, the general lack of understanding about what scientists know and how they know it should be a deep concern to us all. In The Future of Everything, Orrell attempts to give an overview of how scientific modeling has developed, what its shortcomings are, and how far we can really expect mathematical models to predict the future.
Effectively, Orrell starts with the following observation: despite an exponential increase in funding and computing power over the last 100 years, predictive models (particularly in the fields of weather and economic forecasting) have made surprisingly little progress in producing accurate predictions about the future. In fact, modern weather forecasts for beyond a few days are only marginally more accurate, on the whole, than a forecast based on the climatological average for a particular day, in spite of their increasing complexity. Orrell spends much of the book exploring why models fail to give accurate predictions, with climate, the economy, and genetics as his three case studies.
Over the course of the book, Orrell explores a variety of shortcomings in modern mathematical models which aren’t necessarily solved by better computers or more complicated models. Some of the most important ones are (in no particular order):
- Attempting to model complex non-linear systems is mathematically problematic: In the 18th century, mathematical modeling seemed to offer limitless progress. Newton’s laws had transformed a seemingly complicated universe into a few lines of mathematics. If we could predict the course of the stars and planets, surely the world was at our command. Well, not exactly. As it turns out, Newton’s laws of motion turn out to be one of the easiest physical things to model. As Orrell says, part of Newton’s genius was picking a system that was possible to model – the same being true of Gregor Mendel’s study of genetic traits in peas. There may be simple equations for how a planet moves around the sun, but trying to predict how the wind blows (or how a plane flies) is a lot more complicated.
- Chaos: Jeff Goldblum made chaos a trendy term in Jurassic Park, but it remains fairly misunderstood. In modeling, a chaotic system is one where small changes in the initial conditions can dramatically alter the trajectory of the system. Because we can never know the precise initial conditions of a system like the atmosphere or the economy, small perturbations in the initial conditions (or parameters) used in models can have a large effect on the resulting predictions. The fact that model parameterization is often at least somewhat subjective compounds this issue.
- Computational irreducibility: Systems exist which are fairly simple, non-chaotic, produce clear patterns, behave according to only a few rules, and yet are computationally impossible to predict. The best example of this is Conway’s Game of Life. The Game of Life functions according to only four rules, yet it is impossible to write equations which will predict the state of a cell at any arbitrary time. The only way to find out is to run the system.
- Emergent properties: Emergent properties refer to the unpredictable ways which simple entities interact to form complex results. Think “the whole is greater than the sum of its parts.” These emergent properties cannot be simplified to simple physical laws.
- Feedback loops: Most systems have competing positive and negative feedback loops which control the system. One example is blood clotting. Positive feedback is necessary to quickly stop bleeding. If unchecked, all your blood would clot and you would die, so negative feedback slows the process when it reaches an appropriate level. The way feedback loops interact with each other complicates model parameterization.
- Matching the model to past observed data does not ensure accurate predictions: Just because a model matches past observed data does not mean it is correct, nor that it offers any predictive power about the future. A chicken might build a model that predicts a long and happy life based on observations of the farmer coming to feed him every morning. That model holds well, until the day he becomes the farmer’s dinner.
Orrell summarizes as follows:
- Prediction is a holistic business. Our future weather, health, and wealth depend on interrelated effects and must be treated in an integrated fashion.
- Long-term prediction is no easier than short-term prediction. The comparison with reality is just farther away.
- We cannot accurately predict systems such as the climate for two reasons: (1) We don’t have the equations. In an uncomputable system, they don’t exist; and (2) The ones we have are sensitive to errors in parameterization. Small changes to existing models often result in a wide spread of different predictions.
- We cannot accurately state the uncertainty in predictions. For the same two reasons.
- The effects of climate change on health and the economy (and their effects on the climate) are even harder to forecast. When different models are combined, the uncertainties multiply.
- The emergence of new diseases is inherently random and unpredictable. Avian flu may be the next big killer – but a bigger worry is the one that no one has heard about yet.
- Simple predictions are still possible. These usually take the form of general warnings rather than precise statements.
- Models can help us understand system fragilities. A warmer climate may cause tundra to melt and rainforests to burn, thus releasing their massive stores of carbon. However, the models cannot predict the exact probability of such events, or their exact consequences.
So where does that leave us? Orrell again:
Einstein’s theory of relativity was accepted not because a committee agreed that it was a very sensible model, but because its predictions, most of which were highly counterintuitive, could be experimentally verified. Modern GCMs (Global Climate Models) have no such objective claim to validity, because they cannot predict the weather over any relevant time scale. Many of their parameters are invented and adjusted to approximate past climate patterns. Even if this is done using mathematical procedures, the process is no less subjective because the goals and assumptions are those of the model builders. Their projections into the future - especially when combined with the output of economic models – are therefore a kind of fiction. The fact that climate change is an important and contentious issue makes it all the more important that we acknowledge this. The problem with the models is not that they are subjective or objective – there is nothing wrong with a good story, or an informed and honestly argued opinion. It is that they are couched in the language of mathematics and probabilities: subjectivity masquerading as objectivity. Like the Wizard of Oz, they are a bit of a sham.
[A]s I argued in this book, we cannot obtain accurate equations for atmospheric, biological, or social systems, and those we have are typically sensitive to errors in parameterization. By varying a handful of parameters within apparently reasonable bounds, we can get a single climate model to give radically different answers. These problems do not go away with more research or a faster computer; the number of unknown parameters explodes, and the crystal ball grows murkier still. … We can’t mathematically calculate the odds, even if it looks serious, scientific, and somehow reassuring to do so.
Orrell is clear to point out, however, that the fact we cannot guarantee the accuracy of our predictions does not mean they are necessarily wrong, or shouldn’t be heeded. Varying parameters in climate models may in fact produce a wide range of results, but that doesn’t mean we should take a wait and see approach. Economic models failed spectacularly to predict the current economic crisis – but it still happened.
Orrell’s argument, then, is for a kind of literacy when using scientific models to inform decisions. Scientific predictions can be helpful, and often are. But they are limited in their ability to predict future events with certainty, and these problems aren’t necessarily going to be solved with better data and models, or with more powerful computers. They shouldn’t be ignored, but rather viewed for what they are: a tool for helping us understand the present, and hopefully make the best decisions we can about the future.