Incentives for Drivers Who Avoid Traffic Jams

1307 words | 5 page(s)

In his article “Incentives for Drivers Who Avoid Traffic Jams,” published in The New York Times on June 11, 2012, John Markoff argues against imposing a “congestion charge” fee for driving at peak hours in crowded areas. Instead, Markoff argues, an incentives-based program would be more effective, when instead of getting “sticks” (i.e. congestion charges), the drivers will be offered “carrots” (i.e. rewards for a good conduct).To support his main claim, Markoff uses evidence from several experimental studies, which have proved quite productive at preventing people from driving at peak times. In these studies, people got rewarded in cash if they shifted their commuting so that they did not drive at times when congestion peaks or if they chose to come on foot (which also had a positive impact on their health). Markoff also provides an opposing view from an authoritative expert, who thinks that this incentives-based system cannot work on the large scale and will not be sufficient to combat traffic congestion problem in New York and other large cities. Although his argument has sense, Markoff refutes it by providing more examples of the program’s potential effectiveness, especially as he refers to Singapore’s implementation of the idea. He also cites the author of the incentives-based program, Dr.Balaji Prabhakar, a professor at Stanford Univeristy, who says that “stick” and “carrot” methods can co-exist.

Markoff’s article is generally strong at developing its pro-incentive arguments yet it still has a few weaknesses. First of all, Markoff’s argument is fair and balanced. The author avoids using overly emotional or highly loaded language. Having chosen a quite formal style, he remains quite objective all throughout the text, avoiding informal (emotionally expressive) punctuation, appraisals of the reward-based system of managing human conduct, or harsh criticism of the opponents’ view. Instead, he relies on evidence, mostly experts’ opinions, but also citing the preliminary results of the scholarly studies on the issue in question. What’s more, Markoff provides an alternative point of view, when he refers to the opinion expressed by Charles Komanoff, whom he introduces as “a transportation expert who has designed a computer model of New York traffic.” This helps Markoff avoid being one-sided and allows him to sound fair and unbiased.

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Further, his argument is logically developed. Markoff begins his article by explaining the issue and then demolishes this point by providing arguments in support of the opposing perspective. Besides, he ends his article with a conclusion which both sums up his argument and offers a compromise, where there is still room for other ways of handling drivers behaviour (i.e. using “a stick”). This is evident from the last paragraph where Markoff quotes Professor Prabhakar, “This is one of the nicer problems (…). You don’t have to change everyone’s behaviour; in fact, it’s better if you don’t. ” Also, the author uses a relevant, serious enough tone. He is neither sarcastic nor too dramatic, but allows the reader to conclude himself/herself whether he or she would support the new system. At the same time, Markoff’s argument has a logical fallacy. As he lacks accurate, experimentally verified data on the success of the incentives-based approach to handling drivers’ behaviour during peak hours, he seems to have an argument with novelty fallacy. Without reliable findings from any of the studies he cites (his evidence is made up of experts’ opinions) he sounds as if he argued that the new initiative was good mainly because it is new and different rather than because it can bring desirable effect.

Moreover, as he refutes the opposing view, he fails to use data that would show that the new program applies to a large city, but prefers to cite the intentions of Singapore to implement it. In fact, this places the author at risk of a hasty generalization, since none of the studies he uses to support his viewpoint is credible enough to base the whole argument. Specifically, Markoff simply says, “The system significantly lowered congestion,” without acknowledging the study’s credibility or adequacy (when he speaks about Bangalore experience). In this way, without good, scientifically verified evidence, his article’s argument is a bit weak in terms of logic, with two possible fallacies of novelty fallacy and hasty generalization.

A number of concepts from the course apply to the article. First of all, as Markoff describes Dr. Prabhakar’s incentives-based program and London, Stockholm, and Singapore’s “congestion charges,” the reader can relate these types of behaviour to operant conditioning. Both “sticks” and “rewards” are seen as stimuli; through association with a consequence (with regard to these stimuli), a response takes place. In Dr. Prabhakar’s case, a positive reinforcement takes place, as drivers are encouraged to behave appropriately through money rewards and increasing their satisfaction with themselves. On the opposite, in London’s case, the positive punishment takes place, as “congestion charges” aim at weakening the undesired response. Additionally, Dr. Prabhakar’s approach is based on offering extrinsic reinforcements, including money as well as praise from peers (on the social media).

Although this is likely to bring the desired results for some time, there is risk that the motivation to perform will get lower (especially as it works in combination with intrinsic reward, i.e. feeling good because you have done this well). In this case, the extrinsic reward should be applied in the way that it will work as a recognition of the high-quality performance. Just as we have learnt, application of “congestion charges” caused anger among the drivers, who are said to “hate the idea.” This is one of the problems with punishment listed in the notes. In addition, it seems that only Professor Prabhakar’s approach will foster learning in accordance with Social-Cognitive Learning Theory, because it is associated with positive consequences, cognitive processes, and observation of meaningful behaviour of other drivers.

Dr.Prabhakar’s incentives-based approach, if to relate it to events occurring in the modern world, can be effectively applied in a range of settings. As “organizational structures could be created tha guide people toward better behaviour,” this approach seems to have a great potential for school. Instead of focusing on penalizing students for lack of knowledge or failure to prepare for classes, the teachers would structure their class or teaching activity around their focus on the students’ achievements through praise or other incentives. If to think critically, the system offered by Dr.Prabhakar is very good, but it requires a mature society, whose members will be ready to engage in the lotteries or pursue the rewards not because of extrinsic rewards, but because they would want to improve the society and help others by refusing from some of their behaviour patterns. Just as society has not reached such level of moral development, punishment should be applied along with rewards to address all drivers.

My writing process has been based on my aim to logically develop each of five paragraphs, supplying them with sufficient evidence either from the article text or from the notes It requited application of critical thinking and summary skills. Specifically, in the first paragraph I summed up the main claim of the author; in the second one, I evaluated his argument and found both strong and weak points based on my previous knowledge of what makes up a good argument; further, I analysed the article material based on the theory that we learned at class. Also, in the fourth paragraph, I related the article’s argument to the real world situation of school studies’ assessment as well as explained by personal viewpoint regarding the claim. Finally, the fifth paragraph has been based on the whole paper as it has summed up my writing and critical thinking efforts. This paper has been an excellent critical thinking challenge, so I look forward to other similar tasks.

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