fresh, even though the selling price for fresh fish is twice that of frozen fish; large
numbers of boats were fitted for halibut fishing that were only used for two or three days
during the year.
Upon introducing individual tradable quotas, the season immediately expanded to
250 days and profits increased dramatically. With the tradable quotas, fishermen were
guaranteed the right to harvest a certain number of fish, and no longer needed to
compete to be the first to land the fish. Hence, fish could be caught at a rate that allowed
them to be sold fresh for higher prices. This opens up many interesting questions on
coupling resource models with a theory of cap and trade systems incorporating social
and economic components. Specifically,
- How should quotas be priced and distributed?
- What will be the socio-economic consequences of various pricing systems?
- Will these systems be robust to unforeseen shocks in the harvest?
- What impact will the different pricing systems have on the
biology/ecology/environment?
These questions also arise in situations such as quotas for livestock, land-use or
other resources and similar models can be used for quotas applied to emissions and
pollutants.
3.5. Infectious Disease Systems (Fefferman)
Within a network of social contacts, there are two interrelated on-going dynamic
systems that need to be modeled both separately and together: the transmission of
infection and the making/breaking of contacts as a continued social process. The
systems both shift state internally in non-trivial ways: disease dynamics are driven by
epidemiological characteristics of the disease and the underlying network topology, and
the social system is driven by individual preferences in affiliation and also potentially by
larger scale needs of society (e.g. sufficient organizational success for complicated task
completion, rapid dissemination of information/consensus building for decision making,
etc.). Further, these systems also fundamentally impact each other: disease can impact
social contacts by causing affiliations to be made/broken in response to disease status
and the ongoing social processes are responsible for constructing the underlying,
shifting network topology over which infection can spread. For example, healthy
individuals can avoid contact with unwell individuals to avoid exposure or unwell
individuals can temporarily avoid social contact while convalescing, these actions
interrupt routes of transmission, changing the epidemiological outcomes, but also
potentially compromise information pathways or decrease participation in a group
beneath a minimally effective threshold for action. Similarly, individual behaviors driven
only by social motivations can drastically impact the epidemiological burden of society in
general (e.g. reporting to work despite infectious illness in order to ensure meeting a
project deadline and thereby infecting coworkers, etc.). All of these considerations raise
challenging issues of modeling, simulation, and analysis for mathematical scientists.
- Recommendations
We discuss both high-level recommendations (section 4.1) as well as
recommendations for specific areas wherein opportunities may lie for mathematical
sciences to advance sustainability science (section 4.2).