Players in a game are "in equilibrium" if they are rational, and accurately predict other players' strategies. In many experiments, however, players are not in equilibrium. An alternative is "cognitive hierarchy" (CH) theory, where each player assumes that his strategy is the most sophisticated. The CH model has inductively defined strategic categories: step 0 players randomize; and step k thinkers best-respond, assuming that other players are distributed over step 0 through step k Ϫ 1. This model fits empirical data, and explains why equilibrium theory predicts behavior well in some games and poorly in others. An average of 1.5 steps fits data from many games.
Another social science looks at itself Experimental economists have joined the reproducibility discussion by replicating selected published experiments from two top-tier journals in economics. Camerer et al. found that two-thirds of the 18 studies examined yielded replicable estimates of effect size and direction. This proportion is somewhat lower than unaffiliated experts were willing to bet in an associated prediction market, but roughly in line with expectations from sample sizes and P values. Science , this issue p. 1433
Here we provide further details on the replications, the estimation of standardized effect sizes and complementary replicability indicators, the implementation of the prediction markets and surveys, the comparison of prediction market beliefs, survey beliefs, and replication outcomes, the comparison of reproducibility indicators to experimental economics and the psychological sciences, and additional results and data for the individual studies and markets. The code used for the estimation of replication power, standardized effect sizes, all complementary replication indicators, and all results is posted at OSF (https://osf.io/pfdyw/). Replications Inclusion criteriaWe replicated 21 experimental studies in the social sciences published between 2010 and 2015 in Nature and Science. We included all studies that fulfilled our inclusion criteria for:(i) the journal and time period, (ii) the type of experiment, (iii) the subjects included in the experiment, (iv) the equipment and materials needed to implement the experiment, and (v) the results reported in the experiment. We did not exclude studies that had already been subject to a replication, as this could affect the representativity of the included studies. We define and discuss the five inclusion criteria below. Journal and time period: We included experimental studies published in Nature andScience between 2010 and 2015. The reason for focusing on these two journals is that they are typically considered the two most prestigious general science journals. Articles published in these journals are considered exciting, innovative, and important, which is also reflected in their high impact factors. * Number of observations; number of individuals provided in parenthesis. † Replicated; significant effect (p < 0.05) in the same direction as in original study. ‡ Statistical power to detect 50% of the original effect size r. § Relative standardized effect size. * Belief about the probability of replicating in stage 1 (90% power to detect 75% of the original effect size).† Predicted added probability of replicating in stage 2 (90% power to detect 50% of the original effect size) compared to stage 1. * Mean number of tokens (points) invested per transaction. † Mean number of shares bought or sold per transaction.
Reduction of new product development cycle time and improvements in product performance have become strategic objectives for many technology-driven firms. These goals may conflict, however, and firms must explicitly consider the tradeoff between them. In this paper we introduce a multistage model of new product development process which captures this tradeoff explicitly. We show that if product improvements are additive (over stages), it is optimal to allocate maximal time to the most productive development stage. We then indicate how optimal time-to-market and its implied product performance targets vary with exogenous factors such as the size of the potential market, the presence of existing and new products, profit margins, the length of the window of opportunity, the firm's speed of product improvement, and competitor product performance. We show that some new product development metrics employed in practice, such as minimizing break-even time, can be sub-optimal if firms are striving to maximize profits. We also determine the minimal speed of product improvement required for profitably undertaking new product development, and discuss the implications of product replacement which can occur whenever firms introduce successive generations of new products. Finally, we show that an improvement in the speed of product development does not necessarily lead to an earlier time-to-market, but always leads to enhanced products.new product development, time-to-market, new product performance
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