Introduction and external disturbances it can absorb.



It has now been recognized that
nature and humans have always been the ultimate friends- the
so-called coupled Social-Ecological Systems or SESs. Nature has provided humans
with a range of ecosystem services- from grains
for food, fibers for clothes, to wood for shelters – all of which
allowed civilizations to continue existing. As the 20th century
paved the way to a new renaissance,
humans have learned to manage nature- discovering
new ecosystem services (e.g. oil for
transportation) and optimizing old ones (e.g. breeding of high yielding grain crops) 1, 2. 

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Unfortunately, these developments came
with a big trade-off in the form of global environmental change. Rapid industrialization,
urbanization, agricultural intensification, massive forest logging, extensive mining,
and unplanned land use conversion among many others have been credited to increasing
climate variability, biodiversity losses, and land desertification 3, 4.


Though the global or regional scale
impacts of these changes are still relatively miniscule,
ill-effects have begun to simmer among local villages communities. Nature’s
capacity to provide the usual ecosystem services is declining, creating new versions
of SESs which changed the course of many local peoples’ lives.



Science of and Race for Resilience


Because of these uncertainties faced
by humanity’s future, resilience science has recently gained momentum. It aims
to understand how existing SESs, especially in local scales (e.g. villages),
can retain their respective structures and functions despite the threat of
disturbances. Its main presumption recognizes that these existing SESs have a
threshold of internal changes it can undergo and external disturbances it can
absorb. Simply put, the resilience of an SES will be determined by internal or
external factors, or mostly a combination of both. Once this threshold is
crossed, the SES transforms into its “alternate system state” 5, 6, 7, 8,


For example, a hypothetical village
depends on agricultural activities year-round. Its existing system state
depends on rain water during wet season and pumped ground water during dry season.
If there is an increase in village population (internal change), there will be an increase in intensity and
frequency of ground water for domestic and agricultural usage. If there are
more intense droughts (external
disturbance), there will be a significant
decrease in the supply of ground water.  In both cases, the amount of available ground
water may become insufficient to cover the entirety of the dry duration. If
these conditions persist with no necessary interventions made, the village may
be triggered to enter an alternate state.


Alternate system states may have
infinite versions. However, most are “undesired”. An undesired version provides
less quantity and/or quality of the ecosystem services which may not be able to
support the usual activities and livelihoods of humans. For the hypothetical village,
instead of the practiced year-round agricultural production, its new state may
be characterized by the shift to non-agricultural activities during the
dry season because of lack of water
resource. Unfortunately, this is certainly an undesired state if the village
has been totally dependent on their nature’s ecosystem services, particularly
food from farming, even during the dry season. It would not be a surprise if
the village succumbs to poverty, food insecurity, and social disarray with this
new state. 


What if the internal drivers are identified before the SES crosses its
threshold? The village could have been able to regulate the intensity and
frequency of use ground water for their increasing population.


What if the degree of disturbance
that can cause the SES to cross the threshold be determined beforehand? The village
could have implemented necessary interventions such as even if this entails
additional expenses.  


What if the infinite number of
alternate states be reduced and the most probable ones are projected? By doing so, the village will be ready to adapt to
whatever new conditions nature has for them.


These are the major questions which
resilience science can provide significant insights into this race to build resilient SESs.



(Possible) Rise of Artificial Intelligence for Resilience


All fields of science have evolved
since the rise of computers. Computers have helped researchers and
practitioners facilitate more accurate and
prompt answers to key resilience questions. Satellite imaging and remote
sensing have made it easier to track ecological changes (e.g. land cover) happening in an SES. Software programs have made
it faster and more accurate to forecast major disturbances (e.g. typhoons). Computer-facilitated
modelling tools have made it possible to project most-likely alternate states
of large SESs under certain assumptions (e.g.
climate scenarios). With the swift pace of modern learning curves, this
convergence of resilience and computer sciences is expected to increase over
the next few years. 


In particular, the rapid development
of artificial intelligence or AI will highly influence the direction of
resilience science. It is not impossible that all these technologies currently
employed will soon be packaged into one Ultimate
Resilience Tool- a
centralized super computer with the capacity to analyze multi-layers of
datasets from databases and on-ground conditions.


This tool may be able to identify what
internal drivers to regulate, what disturbances to give extra preparation for,
and what to expect with nature’s ecosystem services in a future full of


Imagine this tool simultaneously analyzing
hundreds of internal and external determinants of the resilience of SESs, including major and minor ones! Imagine this tool
processing thousands of SESs data on land use patterns, demographic shifts,
climactic fluctuations, market dynamics, intergenerational interactions and many
others! Best of all, imagine this tool telling what most probable alternate
state the SESs is about to cross, when it
will cross it, and what happens when it finally crossed!


If this tool may indeed become a
reality, it has huge potentials to help local SESs become resilient against
impacts of global environmental changes. For example, this tool could have
immediately warned the hypothetical village that the number of their population
already poses a threat to their ground water. Or, that the intensity of an
incoming drought may be excessive for their existing SESs. It could save the
village from risks of leaving its year-round agriculturally productive state. It
could save the local SES from crossing an alternate state which offers fewer ecosystem services to support the people.


However, it is recognized that the
development of such tool may also threaten the people working with resilience
science. Currently, assessments of the resilience
of various SESs are conducted manually using integrated methodologies from
social and ecological sciences. Teams of researchers and practitioners go to
target SESs to collect onsite data. Surveys,
field samplings, and tons of secondary data collections are done for months.


But who needs to hire a whole team of
researchers and practitioners when a single tool can provide answers to the
same questions? Indeed, having an Ultimate
Resilience Tool may be a necessary evil for the whole academic field. It is
hereby argued that tapping the future of AIs for this kind of endeavor is
needed given the rate of global environmental changes. Local SESs are the most
vulnerable to impacts of these changes. In turn, this will greatly affect the
ecosystem services local people have long depended on. Thus, all options,
especially AI, should be harnessed to make these SESs resilient.



Need (Still) for a Heart


If this tool shows warning sign that
goes “rate of increase in the number of
people in the village will reduce ecosystem services and will eventually cause
the system to enter an undesired state in two years”, local policy makers
and all of the people of the hypothetical village should be alarmed. During the
era without this tool, tons of research will first be conducted in the village
before providing such kinds of warning. Many times, such warning may come too


However, being alarmed is never


ultimate resilience tool will effectively provide concrete answers on what
drivers, what disturbances, and what alternate states the SES confront. However,
local people still have the final decisions on how to contain these drivers,
how to prepare for these disturbances, and how to adapt to these probable alternate states. In short, realizing a resilient
SES still lies in the hearts of the humans and not just from the brain of a tool.


The new breeds of researchers and
practitioners for resilience science should, therefore, learn on how to become
the catalysts of action for these local SESs. They are the ones who could
understand and recognize the most how valuable the information is, how credible the warning is, and how necessary
it is to take action.       


The new breeds of researchers and
practitioners should provide the stimuli to local people into designing
interventions, policies, or actions to implement. They are the ones who can
articulate the implications of such a warning to their daily activities, to
their lives, and to their future.


In the future of resilience science,
this tool will be the primary brain. Yet, the primary heart will be the new
breeds of researchers and practitioners who will harness the maximum potential
of this tool to facilitate local villages and community create resilient SESs.



Road Ahead


While this ultimate resilience tool remains a vision, resilience researchers
and practitioners have never been as more important. Both resilience science and
SES studies themselves are relatively in
their infancy. Thus, more than ever, teams of researchers and practitioners
should be deployed collecting data from the fields and creating huge databases
for these data. These databases will be the foundation of the future tool.


Many of the currently related datasets are from larger scale SESs (e.g.
national and international level). Although these sets may be downscaled by the
future tool for the local SESs, its accuracy and certainty may be compromised.
Thus, having datasets from local contexts is very much necessary.


Resilience science should continue co-evolving
with computer sciences. Resilience researchers and practitioners should
continue utilizing products and future products of computer sciences. Yet still,
they should also start learning how to go beyond their usual academic orb and
give significant practical contributions for local SESs.





Impacts of global environmental
changes are expected to make local SESs cross their thresholds and enter undesired
alternate states. Hence, harnessing the potentials of artificial intelligence
for a future full of uncertainties can be a vital solution in building
resilience. Yet still, these prospects will only be realized if resilience
science continues progressing with
computer science. And when the time comes
for the development of an ultimate resilience tool, resilience researchers and
practitioners should not be threatened. Instead, they should be the prime movers
for local SESs to absorb the information, take action, and move forward.





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