Why is predicting an important element of economics

Also expect the opportunities to be very different from those the majority predicts, for even the most expected futures tend to arrive in utterly unexpected ways.

In the early s, for example, PC makers predicted that every home would shortly have a PC on which people would do word processing and use spreadsheets or, later, read encyclopedias on CDs. But when home PC use did finally come about, it was driven by entertainment, not work, and when people finally consulted encyclopedias on-screen a decade after the PC makers said they would, the encyclopedias were online. The established companies selling their encyclopedias only on CD quickly went out of business.

The entire portion of the S curve to the left of the inflection point is paved with indicators—subtle pointers that when aggregated become powerful hints of things to come. A classic example is the first sales of characters and in-game objects from the online game EverQuest on eBay in the late s. Through the avatars, members engage in social activities, including the creation and sale of in-world objects in a currency Linden dollars that can be exchanged for real dollars through various means.

Where it ends is still uncertain, but it is unquestionably a very large S curve. More often than not, indicators look like mere oddball curiosities or, worse, failures, and just as we dislike uncertainty, we shy away from failures and anomalies.

Its earliest graphical antecedent was Habitat, an online environment developed by Lucasfilm Games in Though nongraphical MUDs multiple user dimensions were a cultish niche success at the time, Habitat quickly disappeared, as did a string of other graphical MUDs developed in the s and s.

Then the tide turned in the late s, when multiplayer online games like EverQuest and Ultima started to take off. It was just a matter of time before the S curve that had begun with Habitat would spike for social environments as well as for games.

So although the explosive success of Second Life came as a considerable surprise to many people, from a forecasting perspective it arrived just about on time, almost 20 years after Habitat briefly appeared and expired.

As the Second Life example illustrates, indicators come in clusters. Department of Defense to design robots that could compete in a mile-plus race across the Mojave Desert. Most of the robots died in sight of the starting line, and only one robot got more than seven miles into the course.

But just 19 months later, at the second Grand Challenge, five robots completed the course. Around the same time I noticed a sudden new robot minicraze popping up that many people dismissed as just another passing fad. What was odd was that my friends with Roombas were as wildly enthusiastic about these machines as they had been about their original K Macs—and being engineers, they had never before shown any interest in owning, much less been excited by, a vacuum cleaner.

Alone, this is just a curious story, but considered with the Grand Challenge success, it is another compelling indicator that a robotics inflection point lies in the not-too-distant future. One indicator: Roomba owners today can even buy costumes for their robots! One of the biggest mistakes a forecaster—or a decision maker—can make is to overrely on one piece of seemingly strong information because it happens to reinforce the conclusion he or she has already reached.

This lesson was tragically underscored when nine U. The bearing placed his ship, the Delphy , north of its dead reckoning position. Convinced that his dead reckoning was accurate, the commander reinterpreted the bearing data in a way that confirmed his erroneous position and ordered a sharp course change towards the rapidly approaching coast.

Nine ships followed the disastrous course. Meanwhile, the deck officers on the Kennedy , the 11th boat in the formation, had concluded from their dead reckoning that they in fact were farther north and closer to shore than the position given by the Delphy. The skipper was skeptical, but the doubt the deck officers raised was sufficient for him to hedge his bets; an hour before the fateful turn he ordered a course change that placed his ship several hundred yards to the west of the ships in front of them, allowing the Kennedy and the three trailing destroyers to avert disaster.

He hedged their bets and, therefore, saved the ship. In forecasting, as in navigation, lots of interlocking weak information is vastly more trustworthy than a point or two of strong information.

The problem is that traditional research habits are based on collecting strong information. And once researchers have gone through the long process of developing a beautiful hypothesis, they have a tendency to ignore any evidence that contradicts their conclusion. This inevitable resistance to contradictory information is responsible in no small part for the nonlinear process of paradigm shifts identified by Thomas Kuhn in his classic The Structure of Scientific Revolutions.

Once a theory gains wide acceptance, there follows a long stable period in which the theory remains accepted wisdom. All the while, however, contradictory evidence is quietly building that eventually results in a sudden shift. Good forecasting is the reverse: It is a process of strong opinions, weakly held. If you must forecast, then forecast often—and be the first one to prove yourself wrong.

The way to do this is to form a forecast as quickly as possible and then set out to discredit it with new data. Your next step is to try to find out why this might not happen. By formulating a sequence of failed forecasts as rapidly as possible, you can steadily refine the cone of uncertainty to a point where you can comfortably base a strategic response on the forecast contained within its boundaries.

Having strong opinions gives you the capacity to reach conclusions quickly, but holding them weakly allows you to discard them the moment you encounter conflicting evidence. Marshall McLuhan once observed that too often people steer their way into the future while staring into the rearview mirror because the past is so much more comforting than the present. McLuhan was right, but used properly, our historical rearview mirror is an extraordinarily powerful forecasting tool.

Consider the uncertainty generated by the post-bubble swirl of the Web, as incumbents like Google and Yahoo, emergent players, and declining traditional TV and print media players jockey for position. It all seems to defy categorization, much less prediction, until one looks back five decades to the emergence in the early s of TV and the subsequent mass-media order it helped catalyze.

The cutting-edge players of the information revolution, from Microsoft to Google, are pedaling every bit as hard. The problem with history is that our love of certainty and continuity often causes us to draw the wrong conclusions. The recent past is rarely a reliable indicator of the future—if it were, one could successfully predict the next 12 months of the Dow or Nasdaq by laying a ruler along the past 12 months and extending the line forward.

You must look for the turns, not the straightaways, and thus you must peer far enough into the past to identify patterns. One must look for the turns, not the straightaways, and thus one must peer far enough into the past to identify patterns. So when you look back for parallels, always look back at least twice as far as you are looking forward. Search for similar patterns, keeping in mind that history—especially recent history—rarely repeats itself directly.

The temptation is to use history as the old analogy goes the way a drunk uses a lamppost, for support rather than illumination. Jerry Levin, for instance, sold Time Warner to AOL in the mistaken belief that he could use mergers and acquisitions to shoulder his company into digital media the way he did so successfully with cable and movies. Another case in point: A dark joke at the Pentagon is that the U. It is a peculiar human quality that we are at once fearful of—and fascinated by—change.

Even in periods of dramatic, rapid transformation, there are vastly more elements that do not change than new things that emerge. Consider again that whirling vortex of the s, the dot-com bubble.

Plenty new was happening, but underlying the revolution were deep, unchanging consumer desires and ultimately, to the sorrow of many a start-up, unchanging laws of economics. By focusing on the novelties, many missed the fact that consumers were using their new broadband links to buy very traditional items like books and engage in old human activities like gossip, entertainment, and pornography.

And though the future-lookers pronounced it to be a time when the old rules no longer applied, the old economic imperatives applied with a vengeance and the dot-com bubble burst just like every other bubble before it. Anyone who had taken the time to examine the history of economic bubbles would have seen it coming. All of the important concepts in this course can be explained without math. That said, math is a tool that can be used to explore economic concepts in very helpful ways.

The use of algebra is a specific way that economics express and explore economic models. Similarly, using the algebraic formula for a line allows economists to find precise points on a graphs that help in interpreting how much of a good should be sold, or at what price.

Why would an economist use math when there are other ways of representing models, such as with text or narrative? Why would you use your fist to bang a nail, if you had a hammer? Math has certain advantages over text. It disciplines our thinking by making us specify exactly what we mean.

At the same time, math has certain disadvantages. Mathematical models lack the nuances that can be found in narrative models. An architect who is designing a major office building will probably build a physical model that sits on a tabletop to show how the entire city block will look after the new building is constructed.

Companies often build models of their new products that are rougher and less finished than the final product but can still demonstrate how the new product will work and look. Such models help people visualize a product or a building in a more complete, concrete way than they could without them. Similarly, economic models offer a way to get a complete view or picture of an economic situation and understand how economic factors fit together.

A good model to start with in economics is the circular flow diagram Figure 2, below. Such a diagram indicates that the economy consists of two groups, households and firms, which interact in two markets: the goods-and-services market also called the product market , in which firms sell and households buy, and the labor market , in which households sell labor to business firms or other employees.

Of course, in the real world, there are many different markets for goods and services and markets for many different types of labor. The circular flow diagram simplifies these distinctions in order to make the picture easier to grasp. In the diagram, firms produce goods and services, which they sell to households in return for payments. With this increased visibility you can analyze your business as a whole with the utmost confidence in the data.

In this blog, we will discuss what forecasting is, why it is important, how it can help your business succeed and tools that can enhance accuracy and simplicity.

Business forecasting consists of tools and techniques used to predict changes in business, such as sales, expenditures, profits and losses.

The goal of business forecasting is to develop better strategies based on these informed predictions; helping to eliminate potential failure or losses before they happen. Forecasting is valuable to businesses because it gives the ability to make informed business decisions and develop data-driven strategies. Financial and operational decisions are made based on current market conditions and predictions on how the future looks.

Past data is aggregated and analyzed to find patterns, used to predict future trends and changes. Forecasting allows your company to be proactive instead of reactive.

Forecasting allows businesses set reasonable and measurable goals based on current and historical data. Having accurate data and statistics to analyze helps businesses to decide what amount of change, growth or improvement will be determined as a success. Having these goals helps to evaluate progress, and adapt business processes where needed to continue on the desired path. There are certain tools such as CRM which will be discussed later in this blog that help to visual forecasting and give insight into things like the sales pipeline, opportunities, and more.

Having visibility into potential trends and changes help businesses to know where to allocate their budget and time spent on certain offerings such as products, services, or areas internally such as hiring and adjusting strategy.

Having insights into current business functionality along with later predicted trends and combining this information into meaningful insights makes for a better allocated and estimated budget.