In this paper, we explore the little studied phenomenon of temporal myopia. In particular, we investigate, using an NK simulation, how three different decision-making patterns – one that focuses solely on the immediate performance consequences (myopic) of a strategic choice, one that focuses solely on the delayed consequences (hypermetropic), and one that balances both the immediate and delayed consequences (bifocal/rational) – promote or hinder the effective discovery of high performing strategies. Our results suggest that counter to common wisdom, the bifocal/rational decision maker, who takes into account all temporal performance consequences of her choices, does not necessarily find the highest performing strategies. Instead, we find that the hypermetropic decision maker, who disregards the short-term, can outperform any other type when there is no uncertainty. Surprisingly, we also find that even temporally myopic decision making can outperform other types under high outcome uncertainty and risk-aversion. Our findings have important implications for how firms should approach strategic search when their choices could have varying temporal performance consequences.
Because networks affect a variety of important firm outcomes, understanding the drivers of network change is fundamental. Research has focused only on two mechanisms by which networks change: tie additions and deletions. Yet these represent a fraction of the six mechanisms by which interorganizational networks can change. Firms also merge, divest, enter industries, and exit industries. From a networks perspective, these events cause nodes to ‘collapse’, ‘split’, ‘appear’, and ‘disappear’. These actions have potentially dramatic effects on the structural position occupied by the focal firm as well as that of other firms whose ties are indirectly affected by the event. We systematically explore how this full set of change mechanisms affects important network outcomes such as openness, closure, and status. Through a simulation, we find that each mechanism has a distinct effect on each outcome, and that the effect depends on whether the action is taken with or without network outcomes in mind. We observe direct effects on the firms involved in the relevant action as well as indirect effects on those not directly involved (i.e. network externalities). This study demonstrates the importance of broadening our conception of the basic mechanics driving network change and identifies several unexplored research areas.
The purpose of this paper is to explore how mental models affect the analyses of dynamic strategic interactions. Our exploration consists of two parts. First, we propose an explanation-based view of mental models founded on a regression analogy. This explanation-based view is inspired by work on revolutions in scientific paradigms (e.g., Kuhn 1970). Second, we implement this view in a series of simulations involving multiple-period Cournot market competitions between two firms which have possibly differing mental models regarding the structure of the market demand. In this context we address the following questions: (1) How sticky are incorrect mental models and how are market observations rationalized to fit such models? (2) What is the impact of different mental models on learning? (3) Can firms with less accurate mental models outperform firms with more accurate mental models and, if so, why? Among other results, we find that incorrect mental models can quite easily “rationalize” market outcomes and that how a firm learns--the rationalization process—depends importantly on the other firm’s mental model. Finally, when the primary inaccuracy of one mental model versus another concerns the slope of demand, the firm with the less accurate mental model typically outperforms the firm with the more accurate mental model.