You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


The postgres_fdw module provides the
foreign-data wrapper postgres_fdw, which
can be used to access data stored in external PostgreSQL servers.

The functionality provided by this module overlaps substantially
with the functionality of the older dblink module. But postgres_fdw provides more transparent and
standards-compliant syntax for accessing remote tables, and can
give better performance in many cases.

Create a foreign server object, using CREATE SERVER, to represent each remote
database you want to connect to. Specify connection information,
except user and password, as options of the server object.

Create a user mapping, using CREATE USER MAPPING, for each
database user you want to allow to access each foreign server.
Specify the remote user name and password to use as user and password options
of the user mapping.

Create a foreign table, using CREATE FOREIGN TABLE, for each
remote table you want to access. The columns of the foreign table
must match the referenced remote table. You can, however, use table
and/or column names different from the remote table's, if you
specify the correct remote names as options of the foreign table
object.

Now you need only SELECT from a foreign
table to access the data stored in its underlying remote table. You
can also modify the remote table using INSERT, UPDATE, or
DELETE. (Of course, the remote user you
have specified in your user mapping must have privileges to do
these things.)

It is generally recommended that the columns of a foreign table
be declared with exactly the same data types, and collations if
applicable, as the referenced columns of the remote table. Although
postgres_fdw is currently rather
forgiving about performing data type conversions at need,
surprising semantic anomalies may arise when types or collations do
not match, due to the remote server interpreting WHERE clauses slightly differently from the local
server.

Note that a foreign table can be declared with fewer columns, or
with a different column order, than its underlying remote table
has. Matching of columns to the remote table is by name, not
position.

A foreign server using the postgres_fdw foreign data wrapper can have the same
options that libpq accepts in
connection strings, as described in Section 31.1.2, except
that these options are not allowed:

user and password (specify these for a user mapping,
instead)

client_encoding (this is automatically
set from the local server encoding)

fallback_application_name (always set
to postgres_fdw)

Only superusers may connect to foreign servers without password
authentication, so always specify the password option for user mappings belonging to
non-superusers.

These options can be used to control the names used in SQL
statements sent to the remote PostgreSQL server. These options are needed
when a foreign table is created with names different from the
underlying remote table's names.

schema_name

This option, which can be specified for a foreign table, gives
the schema name to use for the foreign table on the remote server.
If this option is omitted, the name of the foreign table's schema
is used.

table_name

This option, which can be specified for a foreign table, gives
the table name to use for the foreign table on the remote server.
If this option is omitted, the foreign table's name is used.

column_name

This option, which can be specified for a column of a foreign
table, gives the column name to use for the column on the remote
server. If this option is omitted, the column's name is used.

postgres_fdw retrieves remote data by
executing queries against remote servers, so ideally the estimated
cost of scanning a foreign table should be whatever it costs to be
done on the remote server, plus some overhead for communication.
The most reliable way to get such an estimate is to ask the remote
server and then add something for overhead — but for simple
queries, it may not be worth the cost of an additional remote query
to get a cost estimate. So postgres_fdw
provides the following options to control how cost estimation is
done:

use_remote_estimate

This option, which can be specified for a foreign table or a
foreign server, controls whether postgres_fdw issues remote EXPLAIN commands to obtain cost estimates. A setting
for a foreign table overrides any setting for its server, but only
for that table. The default is false.

fdw_startup_cost

This option, which can be specified for a foreign server, is a
numeric value that is added to the estimated startup cost of any
foreign-table scan on that server. This represents the additional
overhead of establishing a connection, parsing and planning the
query on the remote side, etc. The default value is 100.

fdw_tuple_cost

This option, which can be specified for a foreign server, is a
numeric value that is used as extra cost per-tuple for
foreign-table scans on that server. This represents the additional
overhead of data transfer between servers. You might increase or
decrease this number to reflect higher or lower network delay to
the remote server. The default value is 0.01.

When use_remote_estimate is true,
postgres_fdw obtains row count and cost
estimates from the remote server and then adds fdw_startup_cost and fdw_tuple_cost to the cost estimates. When
use_remote_estimate is false, postgres_fdw performs local row count and cost
estimation and then adds fdw_startup_cost
and fdw_tuple_cost to the cost estimates.
This local estimation is unlikely to be very accurate unless local
copies of the remote table's statistics are available. Running
ANALYZE on the foreign table is the
way to update the local statistics; this will perform a scan of the
remote table and then calculate and store statistics just as though
the table were local. Keeping local statistics can be a useful way
to reduce per-query planning overhead for a remote table — but if
the remote table is frequently updated, the local statistics will
soon be obsolete.

By default all foreign tables using postgres_fdw are assumed to be updatable. This may
be overridden using the following option:

updatable

This option controls whether postgres_fdw allows foreign tables to be modified
using INSERT, UPDATE and DELETE commands.
It can be specified for a foreign table or a foreign server. A
table-level option overrides a server-level option. The default is
true.

Of course, if the remote table is not in fact updatable, an
error would occur anyway. Use of this option primarily allows the
error to be thrown locally without querying the remote server. Note
however that the information_schema views
will report a postgres_fdw foreign table
to be updatable (or not) according to the setting of this option,
without any check of the remote server.

postgres_fdw establishes a connection
to a foreign server during the first query that uses a foreign
table associated with the foreign server. This connection is kept
and re-used for subsequent queries in the same session. However, if
multiple user identities (user mappings) are used to access the
foreign server, a connection is established for each user
mapping.

During a query that references any remote tables on a foreign
server, postgres_fdw opens a transaction
on the remote server if one is not already open corresponding to
the current local transaction. The remote transaction is committed
or aborted when the local transaction commits or aborts. Savepoints
are similarly managed by creating corresponding remote
savepoints.

The remote transaction uses SERIALIZABLE isolation level when the local
transaction has SERIALIZABLE isolation
level; otherwise it uses REPEATABLE READ
isolation level. This choice ensures that if a query performs
multiple table scans on the remote server, it will get
snapshot-consistent results for all the scans. A consequence is
that successive queries within a single transaction will see the
same data from the remote server, even if concurrent updates are
occurring on the remote server due to other activities. That
behavior would be expected anyway if the local transaction uses
SERIALIZABLE or REPEATABLE READ isolation level, but it might be
surprising for a READ COMMITTED local
transaction. A future PostgreSQL
release might modify these rules.

postgres_fdw attempts to optimize
remote queries to reduce the amount of data transferred from
foreign servers. This is done by sending query WHERE clauses to the remote server for execution,
and by not retrieving table columns that are not needed for the
current query. To reduce the risk of misexecution of queries,
WHERE clauses are not sent to the remote
server unless they use only built-in data types, operators, and
functions. Operators and functions in the clauses must be
IMMUTABLE as well.

The query that is actually sent to the remote server for
execution can be examined using EXPLAIN
VERBOSE.

In the remote sessions opened by postgres_fdw, the search_path
parameter is set to just pg_catalog, so
that only built-in objects are visible without schema
qualification. This is not an issue for queries generated by
postgres_fdw itself, because it always
supplies such qualification. However, this can pose a hazard for
functions that are executed on the remote server via triggers or
rules on remote tables. For example, if a remote table is actually
a view, any functions used in that view will be executed with the
restricted search path. It is recommended to schema-qualify all
names in such functions, or else attach SET
search_path options (see CREATE FUNCTION) to such functions to
establish their expected search path environment.

postgres_fdw likewise establishes
remote session settings for the parameters TimeZone, DateStyle, IntervalStyle,
and extra_float_digits.
These are less likely to be problematic than search_path, but can be handled with function
SET options if the need arises.

It is not recommended
that you override this behavior by changing the session-level
settings of these parameters; that is likely to cause postgres_fdw to malfunction.

postgres_fdw can be used with remote
servers dating back to PostgreSQL
8.3. Read-only capability is available back to 8.1. A limitation
however is that postgres_fdw generally
assumes that immutable built-in functions and operators are safe to
send to the remote server for execution, if they appear in a
WHERE clause for a foreign table. Thus, a
built-in function that was added since the remote server's release
might be sent to it for execution, resulting in "function does not exist" or a similar error. This
type of failure can be worked around by rewriting the query, for
example by embedding the foreign table reference in a
sub-SELECT with OFFSET
0 as an optimization fence, and placing the problematic
function or operator outside the sub-SELECT.