Author: Jeremy Christian Tanuan

This article (The coupling relationship between standard development and technology advancement: A game theoretical perspective) presents analyses of the optimization of benefits when comparing trade-offs between technological advancement and standard development. They describe that these two development processes have interests that sometimes conflict based on various factors including culture, economics, efficiency, and corporate competition. Often, the standards can be abused to outshine startup innovation and prevent new technologies from emerging. However, standard development also provides compatibility across technologies, reducing fragmentation. Their aim was to discover, by using game theory, how these processes can be handled to minimize problems and maximize utility.

As we see in Figure 1., they outline the relationship between technical standardization and technological development by showing factors that influence the processes; these either promote or suppress technological advancement. To analyze the relationship, they construct a game matrix as seen in Table 1. where technological advancement chooses a strategy between the columns and standard development chooses between the rows. The usual assumptions that both players simultaneously act and have knowledge of the factors of influence for one another’s strategies hold. As well, the payoff is ordered with standard development’s payoff first and technological advancement’s payoff second. In the game matrix, Behind indicates that the associated player is in a prior state of development compared to the other player and Ahead, the opposite. When both are Behind or Ahead, they are seen to be generally behind/ahead of the industry. The variables in the table are all positive values and reveal two outcomes of possible Nash Equilibria depending on their relative values.

The first outcome is when A > E and D > F; this outcome has two Nash Equilibria: (1) technology advancement is behind standard development and (2) technology advancement is ahead of standard development. In this case, there is a competitive conflict between the two players. However, they mention that competition eventually reaches optimization.

The second outcome is when A < E and D < F; this outcome has one Nash Equilibrium: (1) technology advancement and standard development are at advanced levels. In this case, the two players cooperate and is the optimal stage that would be desired.

Notice, however, that when an entity is in the situation of being Behind in both processes, the only possible route to advance is to take one of the competitive paths rather than the cooperative path; this is often the case for developing societies that do not have the structural maturity to accomplish both immediately.

In the first case they define the technology jungle: where technology advancement is ahead of standard development and any new technology has the possibility of becoming the standard. Table 2. shows a possible matrix of such a situation. Clearly since 8 > 7 and 5 > 4, a Nash Equilibrium resides with the technology advancement ahead of the standard development.

Similarly, they define cultivation: where standard development is ahead of technology advancement; in our example we see the Nash Equilibrium where 6 > 1 and 10 > 9 (Table 2.). This is the case where policies are made before creation of technology.

Neither the technology jungle nor cultivation scenarios are favourable as described earlier where abuse of standards can lead to monopolization and lack of standards leads to fragmentation and wasted resources.

This last case is what would be possible in, as they describe as, an ideal world: where both technology advancement and standard development are ahead. As we can see in Table 3., we have 9 > 2 and 8 > 4 giving us the Nash Equilibrium with both players Ahead. This indicates that any new technology can be compared against standardization and development of standards leads to deployment of technology soon after. They note that this scenario leads to the greatest success of the party employing it. This, however, is difficult to achieve due to various factors especially for newer economies.

It is clear that these cases have huge implications on how societies should operate their development of policies and technology together to optimize their growth. This also presents how powerful game theory can be in helping to distinguish particular scenarios between conflicting processes. This can be helpful for policy makers in making decisions on standard development of technology to promote the best outcome possible for their society.

Reference

Jiang, H., Zhao, S., Yuan, Y., Zhang, L., Duan, L., & Zhang, W. (2018). The coupling relationship between standard development and technology advancement: A game theoretical perspective. Technological Forecasting and Social Change, 135, 169-177. doi:https://doi.org/10.1016/j.techfore.2017.11.018

https://www.sciencedirect.com/science/article/pii/S0040162517315743?casa_token=aXYXApAvZhYAAAAA:qQWtAHJvn5INB5Htfja2KriWAh4Yfe0F13FlS6ep52fo8e4YK0cpA5v5texzGJYKs14SuXLp0w

This article (Leadership networks in Catholic parishes: Implications for implementation research in health) presents differing social networks of two parishes’ leadership structures: Sacred Heart and Holy Cross. The former parish is run by an order and the latter by a diocese. What I found interesting is the clearly distinct difference in distributed leadership vs. leader-centred frameworks respectively by analyzing the collaboration networks of each parish. Evidently, Sacred Heart is shown to have almost complete density of collaboration between their leaders with a density of 0.96 (Fig 1.1); whereas, Holy Cross has a density of 0.65 (Fig 1.2).

Although the sparsity of collaboration in comparison to Sacred Heart would usually indicate less communication overall, the strength of the ties are also taken into consideration. They define strong ties as collaboration on either a daily, weekly, or monthly basis and weak ties on an annual basis. The Strong Triadic Closure property is evidently present in the networks; in fact, all the nodes are a part of a single cluster for each. Despite the stark difference in collaboration density, their corresponding discussion networks have similar density with Sacred Heart having 0.68 and Holy Cross having 0.66. This is, at least in part, due to the proportion of strong ties in Holy Cross’ collaboration network being higher than Sacred Heart’s; clearly, it is significant enough to affect the flow needed to transmit information across the leadership members. They note that ties between leaders at Holy Cross were more likely to be strong, either daily or weekly. This seems to imply an inverse relationship between the necessity of frequent communication and required ties for flow of information.

It seems there is a trade off between the centrality of the ordained leaders and increasing the capacity of the parish to organize programs. This naturally follows from the necessity of additional leadership responsibilities being dispersed to lighten the duties from any single person. At Sacred Heart we see that the three most central leaders in the collaboration network are not ordained but rather religious or laity (Fig 2.1). In contrast, two of the three most central leaders at Holy Cross are ordained (Fig 2.2). To compute the centrality they used flow betweenness centrality which not only accounts for shortest paths but all possible paths. This grants a more accurate depiction of the flow of information however this was likely only possible due to the small sample size; with a larger set of nodes, computing the flow betweenness centrality would be too costly.

Making use of social network analysis has proven to be a useful method of discovering the key members that would be able to share and implement programs. However, it also uncovers some trade offs that should be considered for leadership responsibilities in a parish.

Reference

Negrón, R., Leyva, B., Allen, J., Ospino, H., Tom, L., & Rustan, S. (2014). Leadership networks in Catholic parishes: Implications for implementation research in health. Social Science & Medicine, 122, 53-62. doi:https://doi.org/10.1016/j.socscimed.2014.10.012

https://www.sciencedirect.com/science/article/pii/S0277953614006583